From 52ebea72aab543e6bf161d9a6e83757c751b5c12 Mon Sep 17 00:00:00 2001
From: wassname <1103714+wassname@users.noreply.github.com>
Date: Sun, 5 Jul 2026 08:18:01 +0800
Subject: [PATCH] docs: add Rakhmankulova 2024 Russian moral-vignette
manuscript (pandoc md + media)
Russian validation of the Knutson/Kruepke Realistic Moral Vignettes
(norm violation / social affect / intention), sent by the authors.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
---
docs/mfv_russia/MJ_Manuscript_2024.md | 2029 +++++++++++++++++++++++++
docs/mfv_russia/media/image1.png | Bin 0 -> 178473 bytes
docs/mfv_russia/media/image2.jpg | Bin 0 -> 189348 bytes
docs/mfv_russia/media/image3.jpg | Bin 0 -> 64033 bytes
docs/mfv_russia/media/image4.png | Bin 0 -> 95672 bytes
5 files changed, 2029 insertions(+)
create mode 100644 docs/mfv_russia/MJ_Manuscript_2024.md
create mode 100644 docs/mfv_russia/media/image1.png
create mode 100644 docs/mfv_russia/media/image2.jpg
create mode 100644 docs/mfv_russia/media/image3.jpg
create mode 100644 docs/mfv_russia/media/image4.png
diff --git a/docs/mfv_russia/MJ_Manuscript_2024.md b/docs/mfv_russia/MJ_Manuscript_2024.md
new file mode 100644
index 0000000..2b759dd
--- /dev/null
+++ b/docs/mfv_russia/MJ_Manuscript_2024.md
@@ -0,0 +1,2029 @@
+# Validation of the Russian Version of the Realistic Moral Vignettes for Studies of Moral Judgments
+
+Zorina Rakhmankulova1, Rustam
+Asgarov1,2, Eliana Monahhova3, Semyon
+Mening3,4, Isak B. Blank1, Vasily
+Klucharev1
+
+1 International Laboratory of Social
+Neurobiology, Institute for Cognitive Neuroscience, National Research
+University Higher School of Economics, Moscow, Russia
+
+2 Food and Biotechnology Department,
+Faculty of Engineering, Baku Engineering University, Baku,
+Azerbaijan
+
+3 Centre for Cognition and Decision
+Making, Institute for Cognitive Neuroscience, National Research
+University Higher School of Economics, Moscow, Russia
+
+4 Laboratory for Cognitive Research,
+Faculty of Social Sciences, National Research University Higher School
+of Economics, Moscow, Russia
+
+Zorina Rakhmankulova
+[https://orcid.org/0000-0001-9294-2983](https://orcid.org/0000-0001-9294-2983)
+
+Rustam Asgarov
+[https://orcid.org/0000-0001-6739-6394](https://orcid.org/0000-0001-6739-6394)
+
+Eliana Monahhova
+[https://orcid.org/0000-0001-9426-3642](https://orcid.org/0000-0001-9426-3642)
+
+Semyon Mening
+[https://orcid.org/0009-0007-1740-4932](https://orcid.org/0009-0007-1740-4932)
+
+Vasily Klucharev
+[https://orcid.org/0000-0002-5257-3789](https://orcid.org/0000-0002-5257-3789)
+
+Correspondence concerning this article should be addressed to Eliana
+Monahhova or Rustam Asgarov, Center for Cognition and
+Decision Making, Institute for Cognitive Neuroscience, National Research
+University Higher School of Economics, Krivokolenny Pereulok 3, 101000
+Moscow, Russia. Email:
+[e.monakhova@hse.ru;](mailto:e.monakhova@hse.ru)
+[rustamasgarov@gmail.com](mailto:rustamasgarov@gmail.com)
+
+# Abstract
+
+Moral judgments and behavior are shaped by individual
+experiences and cultural environments. In two online studies, we used a
+standard set of moral vignettes to examine the generalizability of the
+original factor structure of moral judgments by testing two independent
+samples of the Russian population (Study 1, N = 247; Study 2, N =
+223). In Study 1, the exploratory factor analysis revealed three
+components that accounted for most of the variance: norm violation,
+social affect, and intention. In Study 2, the factor structure of the
+identified moral components was validated by confirmatory factor
+analysis. Latent profile analysis revealed five distinct profiles of
+moral scenarios: Peccadillo, Illegal-Antisocial, Controversial Act,
+Prosocial, and a novel profile specific to the Russian sample—Social
+Conflict—as compared to the previous study of the American population.
+These findings suggest fundamental similarities in moral judgment
+processes across cultures while also highlighting culture-specific
+patterns in moral scenario categorization. This study
+also provides researchers with a battery of vignettes that can be used
+in cross-cultural studies of moral judgment.
+
+*Keywords:* vignette, event feature, moral judgment, moral component,
+latent profile
+
+#
+
+# Validation of the Russian Version of the Realistic Moral Vignettes for Studies of Moral Judgments
+
+Human moral judgments and behavior have been extensively studied in
+philosophy, sociology, psychology, and neuroscience. Moral judgments
+enable people to evaluate others’ actions, attitudes, and even
+personalities according to certain social, cultural, and universal
+norms. Some scientific and philosophical frameworks suggest that moral
+judgments and behavior are predominantly influenced and guided by an
+innately operating moral sense of intuitions and emotions, while moral
+reasoning is regarded only as a post hoc cognitive process for
+justifying prior moral judgments when inquired (Haidt, 2001).
+Importantly, an evolutionary perspective suggests that moral judgments
+and morality support within-group altruism, cooperation, and normative
+behavior (Greene & Haidt, 2002) and survival (Hawley, 2003).
+Furthermore, the social intuitionist model proposes that the moral
+judgments and moral behavior of an individual develop in a social and
+cultural environment by the individual’s intuitions formed through
+interpersonal processes and experience (Haidt, 2001). Therefore, social
+and cultural experiences should consequently influence the individual’s
+moral intuitions via reasoned persuasion and social persuasion effects
+(Haidt, 2001). Thus, it is particularly important to develop
+experimental tools to study moral judgments in different cultural
+contexts.
+
+Studies of moral judgments have used different types of stimuli, from
+complex moral dilemmas (Greene et al., 2001), sentences containing
+social norm violations (Heekeren et al., 2003), and moral claims (Moll
+et al., 2001) to highly structured moral scenarios (e.g., Young et al.,
+2007). Few seminal studies have introduced and validated a novel
+experimental paradigm to assess the moral judgments of subjects with
+American cultural background (Escobedo, 2009; Knutson et al., 2010;
+Kruepke et al., 2018). Using three types of cue words for emotions,
+actions, and superlatives, summaries of real-life events with positive
+and negative moral experiences were collected from the participants’
+episodic memories in the form of first-person short moral vignettes
+(Escobedo, 2009). It allowed the creation of a set of standardized
+stimuli in the form of vignettes (Knutson et al., 2010), which are based
+on real-life experiences and improve the ecological validity of future
+studies on moral judgments. Importantly, the findings from 30
+individuals and 312 vignettes (Knutson et al., 2010) were later
+replicated among 661 participants and 117 vignettes divided into three
+subsets of 39 unique vignettes (Kruepke et al., 2018). The factor
+analysis consistently indicated a three-component nature of ratings
+during moral judgments, including norm violation, social affect, and
+intention (Knutson et al., 2010; Kruepke et al., 2018).
+
+Importantly, cultures differ in moral judgments and moral behaviors
+(Graham et al., 2016), and moral foundations theory (Graham et al.,
+2013) emphasizes the role of social learning in moral judgments.
+Furthermore, the individualism–collectivism perspective suggests that
+more individualistic cultures emphasize individual rights, while
+collectivistic cultures reinforce communal obligations and spiritual
+purity (Guerra & Giner-Sorolla, 2010; Graham et al., 2010). For example,
+compared to participants in the United Kingdom,
+Russians demonstrated more collectivistic attitudes (Tower et al.,
+1997), which have been associated with a higher tolerance of deceptive
+behaviors used to avoid conflicts (Seiter et al., 2002). Some streams of
+research have further suggested that compared to Western Europeans,
+Eastern Europeans may hold the belief that multiple, even opposite,
+truths are possible (e.g., Peng & Nisbett, 1999; Varnum et al., 2008).
+Thus, it is possible that the Russian population might make moral
+judgments different from those of the American population used in
+studies that designed the standardized vignette sets currently
+used to probe moral judgments.
+
+In the studies presented herein, we have attempted to replicate previous
+findings using standard moral vignettes (Knutson et al., 2010; Kruepke
+et al., 2018), further validate these vignettes, and examine the
+generalizability of the original factor structure of moral judgments to
+other cultures by testing two independent samples of the Russian
+population. Our research question focuses on examining whether the
+factor structure of moral judgments identified in the American samples
+generalizes to the Russian population. This investigation is motivated
+by prior research showing systematic differences between these cultures
+in value systems and social cognition, particularly regarding
+individualistic versus collectivistic orientations and variations in
+power distance that could influence moral evaluations. We hypothesized
+that while core aspects of moral judgment may be preserved across
+cultures, culturally specific patterns may emerge in how moral scenarios
+are categorized and evaluated.
+
+# Materials and Methods
+
+## Participants
+
+The studies included two samples of Russian-speaking participants
+recruited using social media platforms. *Sample 1* (Study 1) was used as
+a training sample to investigate factor structure via exploratory factor
+analysis (EFA), while *Sample 2* (Study 2) was used as a test sample to
+validate the model identified by EFA using confirmatory factor analysis
+(CFA). Kline (2016) stated that the median sample size for uncomplex
+models is around 200 participants, although having more than 200
+participants is usually preferred. The sample sizes in both Studies 1
+and 2 align with this recommendation.
+
+We invited 261 participants, (ranging in age from 18 to 66 years, mean
+\[*M*\] = 25.0, standard deviation \[*SD*\] = 8.6, 190 females), and 236
+subjects (aged 18 to 66 years, *M* = 26.4, *SD* = 9.7, 162 females) for
+Studies 1 and 2, respectively. Participants accessed the online
+experiment and were provided with relevant online instructions. At the
+beginning of the study, all individuals gave informed consent and
+completed eligibility screening questions. Upon completion of the
+experiment, participants were compensated with 300 monetary units,
+equivalent to approximately 10.4 USD when adjusted for purchasing power
+parity (OECD, 2023). The study was conducted in accordance with the
+Declaration of Helsinki and received approval from the Ethics Committee
+of the HSE University.
+
+All participants self-identified as Russian citizens with permanent
+residency in Russia and confirmed via self-report that they had no
+history of psychiatric or neurological disorders and were not taking
+medication for these conditions at the time of the study. A total of
+223 participants (44.9%) reported completing a
+high school diploma as their highest level of education. Of the
+remaining 274 participants with higher education, 162 participants
+(32.6%) held a bachelor’s degree, 105 participants (21.1%) held a
+master’s degree, and 7 (1.4%) held a doctoral degree. The diverse range
+of degrees spanned various fields, from law, politics, sociology, and
+mathematics to chemistry and economics.
+
+To ensure data quality, we conducted an *insufficient effort responding*
+(IER) analysis using the intra-individual response variability (IRV)
+method (Dunn et al., 2018). We calculated each participant’s IRV score
+by summing the standard deviations of their responses across all scales.
+Potential outliers were identified using both the 1.5 IQR (interquartile
+range) method and the *irv()* function from the ‘careless’ R package
+(Ulitzsch et al., 2022). In Sample 1, we
+identified 12 downward outliers (4.60% of the sample; IDs: 118, 119,
+126, 141, 149, 197, 228, 236, 243, 252, 260, and 261). These
+participants were removed from further analyses. In Sample 2, we
+detected five downward outliers (2.12% of the sample; IDs: 47, 61, 64,
+130, 158) and four upward outliers (1.69% of the sample; IDs: 13, 24,
+104, 151). The downward outliers demonstrated potential
+non-differentiation or straight-lining in ratings, while upward outliers
+demonstrated excessive variability in ratings, possibly due to random or
+inconsistent responses. All identified outliers were excluded from the
+further statistical analyses to enhance the overall integrity of our
+dataset. Additionally, we conducted Mahalanobis distance analysis to
+detect multivariate outliers in both samples that revealed two
+multivariate outliers in Sample 1 (IDs: 2 and 74) and four multivariate
+outliers in Sample 2 (IDs: 26, 35, 43, and 147) that were removed from
+further statistical analyses.
+
+Overall, the final sample size used in all statistical analyses was n =
+247 (age range: 18–66 years, *M* = 25.0, *SD* = 8.3, 183 females) for
+Study 1 and n = 223 (age range: 18–66 years, *M* = 26.4, *SD* = 9.7, 156
+females) for Study 2.
+
+## Moral Vignettes and the Moral Judgment Task
+
+The vignette ratings were collected using an online moral judgment task
+via an online questionnaire, with participants rating 39 vignettes
+regarding 16 event features (dimensions), using 7-point Likert scales
+with fixed-point sliding scales. Each scale was presented as a separate
+question, following Kruepke's (2018) study. In the questionnaire, we
+used moral vignettes from subset-3 (Kruepke et al., 2018), which were
+previously tested on the American sample. Subset-3 was selected due to
+its compatibility with Russian cultural contexts, requiring minimal
+modifications.
+
+Out of 16 event features, the following 10 event features directly
+represented the moral aspects of the vignettes (based on Kruepke et al.,
+2018): emotional intensity (1 = “Not at all emotionally intense” to 7 =
+“Extremely emotionally intense”), emotional aversion (1 = “Not at all
+aversive or unpleasant” to 7 = “Extremely aversive or unpleasant”), harm
+(1 = “No harm to others” to 7 = “Extreme harm to others”), other-benefit
+(1 = “No benefit to others” to 7 = “Extreme benefit to others”),
+self-benefit (1 = “No benefit to main actor” to 7 = “Extreme benefit to
+main actor”), premeditation (1 = “The action was completely unplanned”
+to 7 = “The action was completely planned”), legality (1 = “The action
+was extremely illegal” to 7 = “The action was extremely legal”), social
+norms (1 = “This action breaks social rules” to 7 = “This action follows
+social rules”), socialness (1 = “No other people involved in the action”
+to 7 = “Other people are extremely involved in the action”), and moral
+appropriateness (1 = “Extremely morally inappropriate” to 7 = “Extremely
+morally appropriate”).
+
+Six additional features were more complementary and exploratory in
+nature, providing additional context to the moral judgment aspects:
+frequency (from 1 = “This type of event rarely occurs” to 7 = “This type
+of event occurs all the time”), personal familiarity (from 1 = “Never
+experienced this type of event” to 7 = “Frequently experienced this type
+of event”), general familiarity (from 1 = “Never thought about this type
+of event” to 7 = “Frequently think about this type of event”), self-harm
+(from 1 = “No self-harm towards main actor” to 7 = “Extreme self-harm
+towards main actor”), once vs. repeated event (from 1 = “One-time event”
+to 7 = “Frequently”), and acted differently (from 1 = “Extremely
+unlikely” to 7 = “Extremely likely”).
+
+The vignettes and rating scales (questions and their corresponding
+anchor points) were translated from English into Russian and reviewed by
+the expert to ensure accurate interpretation. Minor adaptations were
+made to a few vignettes to make their content more familiar to the
+Russian audience by adjusting names, places, countries, or currency (for
+representative examples, see **Table 1**). Both the adopted and the
+original versions of the vignettes, along with the event features
+scales, are available online as Supplementary Materials on the Open
+Science Framework: [https://osf.io/9k5b8/](https://osf.io/9k5b8/)
+(**Tables S1** and **S2**).
+
+**Table 1**
+
+*Representative Vignettes Used in the Moral Judgment Task, Including the
+Original Version and the Russian-Specific Version in Russian or English*
+
+| | | | |
+|--------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Vignette No. | Original Version | Russian-Adapted Version | English Translation of Russian Version |
+| 1 | When I first went to **dance** school in **New York** I lied about my age. There is a lot of pressure to be young in the dance industry. I was twenty and told people I was only sixteen. | Когда я только поступила в **школу моделей** в **Москве**, я солгала о своем возрасте. В индустрии моды очень важно быть молодым, поэтому я всем говорила, что мне шестнадцать, хотя в реальности мне было уже двадцать. | When I first went to **fashion** school in **Moscow** I lied about my age. There is a lot of pressure to be young in the **fashion** industry. I was twenty and told people I was only sixteen. |
+| 8 | I am a very unfaithful person in general. I do not have a strong faith in God and I am constantly unfaithful to women. Recently I cheated on my girlfriend who comes from another **state** to see me. | Я в целом очень неверный человек. Я не особо верю в Бога и часто изменяю женщинам. Недавно я изменил своей девушке, которая приехала из другого **города**, чтобы повидаться со мной. | I am a very unfaithful person in general. I do not have a strong faith in God and I am constantly unfaithful to women. Recently I cheated on my girlfriend who comes from another **city** to see me. |
+| 16 | I used to ride the bus everyday to work. One day I noticed a pregnant woman who did not have a seat. So I took her by the arm and helped her find a seat. | Раньше я ежедневно ездил на работу на автобусе. Как-то раз я заметил в автобусе беременную женщину, которой никто не уступал место. Тогда я взял ее за руку и помог найти свободное место. | I used to ride the bus everyday to work. One day I noticed a pregnant woman who did not have a seat. So I took her by the arm and helped her find a seat. |
+
+*Note.* The vignettes were adapted from Kruepke et al. (2018) with
+cultural modifications for the Russian sample.
+
+The comparison of moral vignettes used for Russian and American
+populations supported the validity of using the vignettes originally
+designed for the American population for Russian participants. As shown
+in **Table 2**, moral appropriateness, frequency, personal familiarity,
+and general familiarity revealed close alignment between the Russian and
+American samples. For instance, the measures of moral appropriateness
+and frequency differed only slightly, indicating comparable perceptions
+of the vignettes’ content. Additionally, the ranges for variables such
+as the number of sentences and words remained consistent across both
+samples, further supporting the comparability of the materials. While
+differences in reading ease scores were observed—calculated using the
+Flesch formula in Oborneva’s version for the Russian population
+(Oborneva, 2006) for the Russian sample and the Flesch-Kincaid formula
+(Kincaid et al., 1975; Flesch, 1948) for the American sample—these
+differences were within acceptable limits, reflecting linguistic
+variations rather than fundamental disparities in vignette
+comprehensibility.
+
+**Table 2**
+
+*Characteristics of Moral Vignettes in Russian and American Samples*
+
+
+
+
+
+
+
+
+
+
+
+
+
+| Variable |
+Russians, N = 470
+(Present study) |
+Americans, N = 225
+(Kruepke et al., 2018) |
+
+
+
+M
+ |
+
+SD
+ |
+Range |
+
+M
+ |
+
+SD
+ |
+
+Range
+ |
+
+
+| Moral appropriateness |
+3.88 |
+1.71 |
+1.7–6.6 |
+
+3.56
+ |
+
+1.47
+ |
+
+1.8–6.3
+ |
+
+
+| Frequency |
+4.72 |
+0.77 |
+3.2–6.6 |
+4.63 |
+0.63 |
+3.0–5.7 |
+
+
+| Personal familiarity |
+3.37 |
+0.90 |
+2.1–6.2 |
+2.72 |
+0.85 |
+1.8–4.9 |
+
+
+| General familiarity |
+3.32 |
+0.72 |
+2.2–5.2 |
+2.95 |
+0.66 |
+2.0–4.7 |
+
+
+| Number of sentences |
+2.8 |
+0.5 |
+2–4 |
+3.0 |
+0.2 |
+2–4 |
+
+
+| Number of words |
+36.9 |
+7.1 |
+20–53 |
+42.3 |
+6.1 |
+31–56 |
+
+
+| Number of characters |
+217.6 |
+41.4 |
+117–306 |
+204.9 |
+29.5 |
+154–261 |
+
+
+| Reading ease |
+51.5 |
+17.0 |
+0–85 |
+84.7 |
+9.7 |
+62–99 |
+
+
+
+
+*Note.* *M* = mean across 39 vignettes; *SD* = standard deviation.
+Ranges represent minimum and maximum observed values for each variable.
+The reading ease index ranges from 0 to 100, where higher numbers
+indicate more readable text.
+
+## Statistical Analysis
+
+Two prior studies (Knutson et al., 2010; Kruepke et al., 2018) served as
+the foundation for the statistical analysis. As in these previous
+studies, we began with EFA. Given potential cultural differences between
+Russian and American samples, we hypothesized that the factor structure
+might differ between our study and past findings (Knutson et al., 2010;
+Kruepke et al., 2018). Next, extending previous studies, we applied both
+CFA and exploratory structural equation modeling (ESEM) as a more robust
+approach to confirm the factor structure suggested by EFA and to
+identify the best-fitting model. Finally, we conducted a latent profile
+analysis (LPA) and compared these results with those of the American
+population.
+
+Overall, our data analysis followed three sequential steps, all
+implemented within the R software environment (version 4.2.2; R Core
+Team, 2022), as described below.
+
+1) EFA on the first sample (N = 247; Study 1) was used to identify the
+ factor structure for the concurrent ratings of the vignettes for the
+ Russian population and to compare it to the factor structure of
+ ratings of the vignettes for the American population.
+
+2) CFA and ESEM was performed on the second sample (N = 223; Study 2)
+ to validate the EFA results and to identify the model with the best
+ fit.
+
+3) LPA on the aggregate sample (N = 470) was conducted to characterize
+ patterns of the Russian participants’ responses to the vignettes and
+ to compare these profiles with those found for the American
+ population.
+
+Given our focus on the moral components of the vignettes, all of our
+analyses, similar to Knutson et al. (2010), were conducted on the 10
+event features—emotional intensity, emotional aversion, harm,
+other-benefit, self-benefit, premeditation, legality, social norms,
+socialness, and moral appropriateness.
+
+# Results
+
+## Exploratory Factor Analysis (Study 1)
+
+The EFA was performed using the “psych” (Revelle, 2023) and the
+“EFAtools” (Steiner & Grieder, 2020) R packages. The ratings of the 10
+moral event features were averaged across participants to obtain mean
+values for the event features per vignette. To account for the skewness
+of our data, detected by Mardia’s test of multivariate normality (*p* \<
+.001; Mardia, 1970), we proceeded with a Spearman correlation matrix.
+The Kaiser-Meyer-Olkin measure (.72), above the recommended minimum
+value of .50 (Kaiser & Rice, 1974) and significant Bartlett’s test of
+sphericity (χ2(45) = 445.99, *p* \< .001; Bartlett, 1951),
+indicated satisfactory overall sampling adequacy and significant
+correlations among the event feature variables. The optimal number of
+factors to extract was determined through several eigenvalue-based
+criteria: Cattell’s scree test (Cattell, 1966), the Kaiser-Guttman rule
+(Guttman, 1954; Kaiser, 1960), and a more recent adaptation of parallel
+analysis via principal axis factoring (see **Figure 1**; Crawford et
+al., 2010; Lim & Jahng, 2019). All these tests collectively pointed to a
+three-factor solution, accounting for 76% of the total variance, as the
+most plausible fit for our data.
+
+**Figure 1**
+
+*Scree Plot with Parallel Analysis Results*
+
+
+
+*Note.* The solid line represents the scree plot derived from the actual
+data, while the dashed line illustrates the scree plot for randomly
+simulated data with the same dimensions as the actual data. This figure
+clearly indicates that three factors should be retained, as the first
+three factors are distinctly above the random scree plot.
+
+The factor loadings for the 10 event features obtained using the
+principal axis factoring method with Varimax rotation and Kaiser
+normalization (Kaiser, 1958) are displayed in **Table 3**. Additionally,
+**Table 3** includes the factor loadings identified in the two prior
+studies (Knutson et al., 2010; Kruepke et al., 2018) on the American
+population, allowing for a direct comparison of the two populations.
+Similar to the previous studies (Knutson et al., 2010; Kruepke et al.,
+2018), the strongest common factors derived from EFA can be interpreted
+as norm violation (factor 1), social affect (factor 2) and intention
+(factor 3). Norm violation, with positive loadings from social norms,
+moral appropriateness, legality, and other-benefit event features, along
+with negative loadings from harm and emotional aversion event features,
+explained the largest part of total variance (46%). Social affect, which
+is primarily characterized by positive loadings from emotional
+intensity, emotional aversion, socialness, harm event features, and a
+negative loading from the self-benefit event feature, accounted for 17%
+of the total variance. The third factor, intention, explained only 12%
+of the variance and was driven by positive loadings from premeditation
+and self-benefit event features.
+
+**Table 3**
+
+*Results of Exploratory Factor Analyses (EFA) for the Russian (current
+study) and American Populations (from Knutson et al., 2010; Kruepke et
+al., 2018)*
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+| Event Features |
+Russians, N= 247
+Subset-3: 39 vignettes
+(Present study) |
+Americans, N=225
+Subset-3: 39 vignettes
+(Kruepke et al., 2018) |
+Americans, N=30
+Full set: 312 vignettes
+(Knutson et al., 2010) |
+
+
+| Norm Violation |
+Social Affect |
+Intention |
+Norm Violation |
+Social Affect |
+Intention |
+Norm Violation |
+Social Affect |
+Intention |
+
+
+| Social norms |
+.970 |
+-.160 |
+-.024 |
+.956 |
+-.179 |
+-.023 |
+.947 |
+.154 |
+.144 |
+
+
+| Moral appropriateness |
+.968 |
+-.208 |
+.023 |
+.951 |
+-.193 |
+-.042 |
+-.956 |
+-.102 |
+-.120 |
+
+
+| Legality |
+.814 |
+.154 |
+-.083 |
+.785 |
+.335 |
+-.046 |
+.737 |
+-.288 |
+.115 |
+
+
+| Other-benefit |
+.806 |
+.080 |
+-.026 |
+.898 |
+-.111 |
+-.054 |
+-.883 |
+.046 |
+.051 |
+
+
+| Harm |
+-.864 |
+.436 |
+-.022 |
+-.814 |
+.460 |
+-.048 |
+.803 |
+.473 |
+.009 |
+
+
+| Emotional aversion |
+-.788 |
+.588 |
+-.035 |
+-.521 |
+.788 |
+-.012 |
+.336 |
+.762 |
+-.258 |
+
+
+| Emotional intensity |
+-.132 |
+.698 |
+-.018 |
+-.213 |
+.896 |
+.067 |
+.024 |
+.896 |
+-.066 |
+
+
+| Socialness |
+.030 |
+.572 |
+-.007 |
+.087 |
+.712 |
+-.116 |
+-.115 |
+.763 |
+.154 |
+
+
+| Premeditation |
+-.138 |
+.134 |
+.799 |
+-.001 |
+.201 |
+.914 |
+-.002 |
+.175 |
+.859 |
+
+
+| Self-benefit |
+.181 |
+-.529 |
+.731 |
+-.069 |
+-.371 |
+.813 |
+.244 |
+-.304 |
+.772 |
+
+
+| Variance explained |
+46% |
+17% |
+12% |
+48% |
+21% |
+14% |
+40% |
+24% |
+15% |
+
+
+
+
+*Note.* Loadings of \> 0.4 are in bold. In the Kruepke et al. (2018) and
+present studies scales for social norms and illegality were reversed in
+comparison with the study of Knutson et al. (2010), so that higher
+scores mean higher compliance with social norms and higher legality,
+respectively. However, despite the difference in loading direction for
+the norm violation factor, the results reflect the same factor structure
+and interpretation.
+
+Furthermore, we used one extra orthogonal (*geominT*) rotation and
+several different oblique (*promax, geominQ, oblimin*) rotations. These
+alternative rotations yielded very similar three-factor solutions, as
+detailed in supplementary **Table S3** (see Supplementary Materials).
+However, the relatively weak correlations among the extracted factors
+(ranging from 0.01 to 0.33; *M* = 0.14, *SD* = 0.16) obtained with the
+oblique rotations led us to consider the factors as independent;
+therefore, orthogonal rotations seemed to be more appropriate for our
+dataset.
+
+According to the loading cutoff value of 0.4 (Hair et al., 2010), we
+observed three moderate-size cross-loadings
+for emotional aversion, harm, and self-benefit event features. Emotional
+aversion and harm loaded on norm violation and social affect factors,
+while self-benefit loaded on social affect and intention factors. It is
+important to highlight that Kruepke et al. (2018) also demonstrated
+cross-loadings for emotional aversion and harm. The cross-loading for
+the self-benefit variable was not observed in the American population,
+given a threshold of 0.4, although its contribution to social affect was
+only slightly below the cutoff (-.371). The overall similarity of the
+factor loadings between the Russian and American subjects was assessed
+using Tucker’s index of factor congruence (Burt, 1948; Tucker, 1951).
+All three factors exhibited very high values of Tucker’s index ($\geq$
+0.95; **Table S4** in Supplementary Materials), suggesting a high degree
+of similarity of the factor structures and loadings (Lorenzo-Seva & Ten
+Berge, 2006) across the studies with Russian and American samples.
+
+Furthermore, given the gender disparity in our sample (74% females) and
+the potential influence of gender on moral judgments, we examined the
+factor congruence between the male and the female groups in our dataset.
+The correspondence was very high, ranging from 1.00 to 0.98 (**Table
+S4** in Supplementary Materials), implying that the gender imbalance in
+our sample should not be seen as having a distorting effect on the EFA
+results.
+
+## Confirmatory Factor Analysis (Study 2)
+
+To our knowledge, CFA has not been presented in the previous studies of
+moral vignettes. Since EFA does not provide a detailed assessment of
+error terms or a rigorous test of how well the identified structure fits
+the observed data, CFA is required to confirm the
+correctness of the model obtained during the exploratory phase of
+the data analysis.
+
+In standard CFA, it is often assumed that there is a simple factor
+structure: each factor is determined by its unique set of indicators,
+ideally without an overlap, leading to most or all cross-loadings being
+fixed at zero (McDonald, 1985). However, even minor model inaccuracies
+(e.g., not accounting for small cross-loadings such as .10 or .15) can
+significantly affect the rest of the model, resulting in a bias toward
+higher correlations between CFA factors and a poor fit to the data that
+has been demonstrated for simulations (Asparouhov & Muthén, 2009; Marsh
+et al., 2013) and real data (Marsh et al., 2010; Spooren et al., 2010).
+The presence of at least three moderate cross-loadings in our
+exploratory factor structure indicated that a simple CFA structure might
+be too restrictive for our dataset (Morin et al., 2013). Therefore, in
+addition to standard CFA, we also applied ESEM (Asparouhov & Muthén,
+2009), which integrates the advantages of both EFA and CFA (Marsh et
+al., 2014). Unlike the traditional approach, ESEM does not require the
+elimination of even small cross-loadings, which could be theoretically
+justified. This makes ESEM generally more suitable for complex factor
+structures with multiple cross-loadings between factors. Importantly,
+ESEM allowed us to achieve proper model estimation by avoiding
+overfitting related to the misuse of residual covariances, convergence
+problems, and goodness-of-fit challenges.
+
+The СFA and ESEM were performed using the “*lavaan*” (Rosseel, 2012) and
+“*esemComp*” (Silvestrin & de Beer, 2024) R packages. Prior to these
+analyses, similar to EFA, the ratings of the 10 moral event features
+were averaged across participants to obtain mean values for each event
+feature for each vignette. In the CFA, the event features were assigned
+to their predefined constructs (i.e., norm violation, social affect, or
+intention) based on loadings exceeding 0.4, as identified in the EFA in
+Study 1 (see **Table 2**). Consequently, the CFA model included only
+three cross-loadings—harm, emotional aversion, and
+self-benefit—consistent with those observed in the EFA. By contrast, in
+the ESEM, the event features were freely estimated and allowed to
+cross-load onto multiple factors. A *geominT* rotation was employed in
+the ESEM, as it does not prioritize the elimination of cross-loadings
+(Asparouhov & Muthén, 2009). Consistent with the EFA results, the
+factors in the models were assumed to be independent.
+
+All models were estimated using a robust version of the maximum
+likelihood estimator (MLR) with standardized latent variables, where the
+variance of each latent variable was fixed at 1.0 (Brown, 2015). Models’
+goodness-of-fit was assessed using several traditional indices,
+including the scaled chi-square statistic (scaled χ2; Yuan &
+Bentler, 2000), robust comparative fit index (CFI), robust Tucker-Lewis
+index (TLI), robust standardized root mean square residual (SRMR), and
+robust root mean square error of approximation (RMSEA) with its 90%
+confidence interval. Traditionally, a good fit is indicated by values
+less than .08 for RMSEA and SRMR (Hu & Bentler, 1999) and by values of
+.95 or higher for CFI and TLI, although values around .90 can be
+acceptable (Hoyle, 1995). The scaled chi-square statistic tests the
+model’s exact fit to the data (Weston & Gore, 2006). In addition, we
+used the scaled chi-square difference test (Satorra & Bentler, 2001),
+based on the standard chi-square statistic, to compare nested competing
+models in CFA and ESEM.
+
+The goodness-of-fit indices for the CFA and ESEM models, presented in
+**Table 4**, indicated that neither solution generally fit the data
+satisfactorily. Specifically, the CFA showed poor fit across all
+indices, whereas the ESEM demonstrated acceptable fit on the CFI, TLI,
+and SRMR indices but not on the RMSEA. An inspection of the modification
+indices (MI) suggested that inclusion of four residual covariances with
+MI greater than or equal to five in the ESEM model (socialness and
+legality, socialness and other-benefit, social norms and other-benefit,
+and other-benefit and harm) and four residual covariances in the CFA
+model (the first three as in the ESEM model, plus social norms and
+legality) significantly improved model fit (CFA:
+χ2diff = 31.87, df diff = 4, *p* \<
+.001; ESEM: χ2diff = 25.83, df diff =
+4, *p* \< .001).
+
+**Table 4**
+
+*Goodness-of-Fit Statistics for ESEM and CFA Models*
+
+
+
+
+
+
+
+
+
+
+
+
+
+| Model |
+scaled χ2 |
+df |
+CFI |
+TLI |
+SRMR |
+RMSEA
+(90% CI) |
+
+
+| CFA |
+122.914, p < .001 |
+35 |
+.828 |
+.778 |
+.138 |
+.256 (.208; .306) |
+
+
+| ESEM |
+50.413, p = .001 |
+24 |
+.948 |
+.903 |
+.046 |
+.169 (.103; .234) |
+
+
+CFA,
+4 residual covariances* |
+92.935, p < .001 |
+31 |
+.877 |
+.821 |
+.130 |
+.230 (.177; .284) |
+
+
+ESEM,
+4 residual covariances* |
+20.237, p = .443 |
+20 |
+1.000 |
+.999 |
+.031 |
+.017 (.000; .137) |
+
+
+
+
+*Note.* Optimal models are marked with an asterisk. Scaled χ2
+= scaled chi-squared statistic; χ2 = normal chi-squared
+statistic; df = degrees of freedom; CFI = Comparative Fit Index; TLI =
+Tucker-Lewis Index; SRMR = Standardized Root Mean Square Residual; RMSEA
+= Root Mean Square Error of Approximation.
+
+The inclusion of residual covariances in the ESEM and CFA models was not
+only statistically motivated but also theoretically grounded.
+Covariances reflect key relationships: socially involved actions are
+more likely to align with laws and benefit others due to their
+regulation and alignment with collective goals (Cialdini et al., 1990;
+Tyler, 2006; Axelrod & Hamilton, 1981). Similarly, laws often formalize
+social norms that promote altruism and reduce harm (Haidt, 2012;
+Schwartz, 1977). Actions benefiting others are less likely to be viewed
+as harmful, aligning with dualistic moral perceptions (Fehr &
+Fischbacher, 2003).
+
+Although the two solutions that included residual covariances produced
+the expected factor structure (see **Table 5**), only the ESEM model
+displayed adequate goodness-of-fit statistics, including a
+non-significant chi-squared test of exact fit, CFI/TLI \> 0.95, and
+SRMR/RMSEA \< 0.05 (see **Table 4** for details). These results
+emphasize the superiority of the ESEM solution over CFA in achieving a
+better and more acceptable model fit.
+
+**Table 5**
+
+*Standardized Loadings for the CFA and ESEM Optimal Models*
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+| Event Features |
+
+CFA, 4 residual covariances
+ |
+ESEM, 4 residual covariances |
+
+
+| Norm Violation |
+Social Affect |
+Intention |
+(resid) |
+Norm Violation |
+Social Affect |
+Intention |
+(resid) |
+
+
+| Social norms |
+
+.992
+ |
+ |
+ |
+
+(.016)
+ |
+
+.997
+ |
+
+-.004
+ |
+
+.003
+ |
+
+(.006)
+ |
+
+
+| Moral appropriateness |
+
+.993
+ |
+ |
+ |
+
+(.014)
+ |
+
+.986
+ |
+
+-.147
+ |
+
+.029
+ |
+
+(.005)
+ |
+
+
+| Legality |
+
+.690
+ |
+ |
+ |
+
+(.523)
+ |
+
+.732
+ |
+
+.287
+ |
+
+.210
+ |
+
+(.337)
+ |
+
+
+| Other-benefit |
+
+.795
+ |
+ |
+ |
+
+(.368)
+ |
+
+.773
+ |
+
+-.024
+ |
+
+-.214
+ |
+
+(.356)
+ |
+
+
+| Harm |
+
+-.910
+ |
+
+.327
+ |
+ |
+
+(.064)
+ |
+
+-.902
+ |
+
+.355
+ |
+
+-.061
+ |
+
+(.056)
+ |
+
+
+| Emotional aversion |
+-.898 |
+
+.409
+ |
+ |
+
+(.026)
+ |
+
+-.885
+ |
+
+.456
+ |
+
+.015
+ |
+
+(.008)
+ |
+
+
+| Emotional intensity |
+ |
+
+.738
+ |
+ |
+
+(.455)
+ |
+
+-.086
+ |
+
+.702
+ |
+
+.031
+ |
+
+(.499)
+ |
+
+
+| Socialness |
+ |
+
+.449
+ |
+ |
+
+(.798)
+ |
+
+-.013
+ |
+
+.453
+ |
+
+-.014
+ |
+
+(.795)
+ |
+
+
+| Premeditation |
+ |
+ |
+
+.789
+ |
+
+(.378)
+ |
+
+-.182
+ |
+
+.042
+ |
+
+.828
+ |
+
+(.280)
+ |
+
+
+| Self-benefit |
+ |
+
+-.605
+ |
+
+.584
+ |
+
+(.293)
+ |
+
+.315
+ |
+
+-.551
+ |
+
+.612
+ |
+
+(.222)
+ |
+
+
+
+
+*Note.* Loadings of \> 0.4 are in bold; (resid) = event feature
+residual.
+
+Overall, the best-fitting ESEM model confirmed the factor structure and
+variable loadings from the EFA, with most event features loading
+strongly (\> 0.4; p \< .001) onto their respective factors. Three
+cross-loadings were identified: emotional aversion onto norm violation
+(β = -.90) and social affect (β = .41), self-benefit onto intention (β =
+.61) and social affect (β = -.55), and harm onto norm violation (β =
+-.90) and weakly onto social affect (β = .36). Although these
+cross-loadings generally aligned with the American sample, emotional
+aversion had a stronger loading on norm violation in the Russian sample
+than in the American sample, where it was primarily associated with
+social affect (see **Table 3** for details). Additionally, the
+cross-loading of self-benefit was more pronounced in the Russian sample,
+whereas that of harm was stronger in the American sample. **Figure 2**
+shows the graphical representation of the best-fitting ESEM model.
+
+**Figure 2**
+
+*ESEM Optimal Model with Four Residual Covariances*
+
+
+
+*Note.* Latent factors (NormV = norm violation, SocAf = social affect,
+Inten = intention) are in circles; event features (ScNorm = social
+norms, MrApp = moral appropriateness, Legal = legality, OtBen =
+other-benefit, EmAv = emotional aversion, EmInt = emotional intensity,
+Socil = socialness, Premd = premeditation, SlfBn = self-benefit) are in
+rectangles. Positive and negative loadings are colored in green and red,
+respectively, with opacity reflecting the magnitude of the loadings.
+
+## Latent Profile Analysis
+
+Following EFA and CFA, we also conducted an LPA using concurrent event
+feature ratings of the vignettes averaged across the participants. The
+goal of this analysis was to explore unique patterns of the
+participants’ responses to vignette stories based on their event feature
+ratings. We included ratings of the event features of emotional
+intensity, emotional aversion, harm, other-benefit, self-benefit,
+premeditation, legality, social norm, and socialness as indicator
+variables and excluded ratings of the event feature moral
+appropriateness for empirical categorization of the vignette stories
+(i.e., clustering of the vignette responses) in the form of latent
+profiles. The event feature moral appropriateness was excluded from our
+model-based clustering and classification analysis to explore it as an
+external variable and assess/compare the profile memberships predicted
+by this variable (Fraley & Raftery, 1998;
+2002). We used the “*mclust*” package (Scrucca et
+al., 2016) to identify the best fitting model and profile numbers
+using the Bayesian information criterion (BIC; Schwarz, 1978). Model
+fitting and cluster analysis of the data by simultaneous application of
+all 14 Gaussian mixture models of the “*mclust*” package revealed two
+models with the lowest absolute BIC values–model VII and model VEI–with
+various numbers of profiles. The best-fitting models with the
+corresponding BIC values and profile numbers are presented in **Table
+6**.
+
+**Table 6**
+
+*Gaussian Mixture Modeling of the Ratings of the Vignette Event Feature*
+
+
+
+
+
+
+
+
+
+| Gaussian Mixture Models |
+Latent Profiles/
+Clusters |
+BIC |
+
+
+| VEI (diagonal, varying volume, equal shape) |
+8 profiles |
+-899.21 |
+
+
+| VEI (diagonal, varying volume, equal shape) |
+5 profiles |
+-929.19 |
+
+
+| VII (spherical, unequal, volume) |
+5 profiles |
+-929.40 |
+
+
+
+
+For further LPA, we chose the second best-fitting model VEI with five
+profile solutions, as the model VEI with eight profile solutions did not
+reveal a statistically meaningful and theoretically interpretable
+profile solution for our vignette ratings data. Subsequent clustering
+and classification analysis of vignette’s ratings data with the model
+VEI showed five latent profiles (BIC = -929.19) as the best solution,
+followed by seven profiles (BIC = 951.26) and 6 profiles (BIC =
+-969.38). Five-profile solution clustered 17 vignettes in latent profile
+1 and 3 in latent profile 2, while latent profiles 3 and 4 each
+clustered 7 different vignettes and latent profile 5 clustered the
+remaining 5 vignettes together. The cluster plots of the vignettes based
+on the model VEI with five profiles are shown in **Figure 3**. The
+latent profile classification of the vignettes across all five latent
+profiles (**Table S1**), as well as BIC value plots for all Gaussian
+mixture models’ profile solutions (**Figure S2**), are provided in the
+Supplementary Materials.
+
+**Figure 3**
+
+*The Cluster Plot for the Vignettes Across Five Latent Profiles Based on
+the Model VEI*
+
+
+
+*Note.* Individual vignettes are denoted by red dots, marsh triangles,
+green squares, blue pluses, or pink crosses stand for classification in
+the latent profiles 1–5, respectively. The profiles are depicted with
+color-coded ellipses and ellipsoidal centers; each profile is denoted by
+the cluster number in the figure legend. Сlusters 1:5 in the figure
+legend denote profiles 1:5, respectively.
+
+The identified latent profiles were labeled Peccadillo, Illegal &
+Antisocial, Controversial Act, Prosocial, and Social Conflict based on
+the previous literature. The profile Social Conflict was not identified
+in Kruepke et al.’s (2018) original study; however, it emerged as a
+highly robust category in our Russian sample, consistently appearing
+across both VEI and VII models. Notably, our analysis did not yield the
+Deception profile found in Kruepke et al.’s (2018) study; most vignettes
+previously categorized as Deception were absorbed into the Peccadillo
+profile in our sample. **Figure 4** presents boxplots comparing the
+distribution of ratings across each event feature for the observed
+latent profiles, allowing for a direct comparison of how profiles differ
+on individual moral dimensions. The vignette ratings for moral
+appropriateness were combined with the latent profile classification
+results to demonstrate both the quantitative and qualitative results.
+
+**Figure 4**
+
+*Gaussian Mixture Modeling for Classification of the Vignette Ratings
+across Nine Event Features as Indicator Variables*
+
+
+
+*Note.* Five latent profiles were identified and labeled as Peccadillo,
+Social Conflict, Controversial Act, Prosocial, and Illegal & Antisocial,
+based on a theoretical interpretation of the results. The vignettes’
+ratings of moral appropriateness are included to illustrate the original
+behavioral results.
+
+Thus, 21 vignettes out of a total of 39 vignettes were clustered and
+classified in the Peccadillo profile based on the participant ratings.
+The profile is characterized by relatively low ratings of emotional
+aversion, harm, and other-benefit. The Social Conflict profile clustered
+only three vignettes, depicting situations with interpersonal conflicts
+that occurred accidentally and unintentionally. Thus, the Social
+Conflict profile is characterized by relatively low ratings of
+premeditation, self-benefit, moral appropriateness, social norms, and by
+relatively high harm and socialness. The Controversial Act profile
+clustered five vignettes for which actions were perceived as having
+elevated levels of harm and premeditation, violation of social norms,
+and low moral appropriateness. The Prosocial profile clustered five
+vignettes for which actions were perceived as having nearly neutral
+emotional intensity, relatively low emotional aversion, high
+other-benefit (as compared to self-benefit), high socialness, legality,
+moral appropriateness, and compliance with social norms. Finally, the
+Illegal & Antisocial profile clustered five vignettes for which the
+actions were rated by participants as demonstrating high levels of harm,
+high emotional aversion, relatively high self-benefit (as compared to
+other-benefit), low legality and moral appropriateness, violating social
+norms.
+
+Finally, we conducted multinomial logistic regression analyses to
+explore how profile membership (identified in LPA) was predicted by
+moral appropriateness ratings as an external variable. Using the
+vignettes’ profile numbers, profile probability values, and averaged
+ratings ("nnet" package; Venables & Ripley, 2002), we sequentially
+assigned each profile as a reference category. For example, the results
+revealed that vignettes with lower moral appropriateness ratings had
+significantly higher odds of being classified in the Controversial Act
+and Illegal & Antisocial profiles compared to the Peccadillo profile (p
+= *.038* and p = *.039*, respectively). Full results of the multinomial
+regression analyses are provided in supplementary **Table S5** in
+Supplementary materials.
+
+# Discussion
+
+We conducted two online studies of Russian participants (Study 1, N =
+247 and Study 2, N = 223) who rated 39 moral vignettes on 10 moral event
+features (dimensions). We translated and validated
+standard sets of vignettes (Knutson et al., 2010; Kruepke et al.,
+2018) that provide a tool to assess the main event
+features known to influence moral judgments and can be further used in
+behavioral, neuroimaging, and cross-cultural studies. The
+original vignettes were developed based on real-life episodic memories,
+and each vignette event in the sets is characterized by previously
+well-defined key moral features (Rusbult & Van Lange, 1996; Haidt,
+2007). Our results demonstrate the reproducibility of ratings of
+vignettes’ moral features for Russian, American and Iranian
+participants, but they also indicate some cross-cultural differences.
+Using qualitative and quantitative statistical analyzes, we observed
+highly similar responses in the evaluation of the vignettes’ event plots
+and event features by our Russian participants (current study) compared
+to American and Iranian individuals (Knutson et al*.*, 2010; Kruepke et
+al., 2018; Yazdanpanah et al., 2021).
+
+Similar to previous studies, our EFA of the event feature ratings
+confirmed norm violation, social affect, and intention as fundamental
+components of moral judgement. The
+significance of our findings for understanding the basis of human moral
+judgement is severalfold. Our findings regarding cross-culturally shared
+moral motives confirm the role of universally recognized basic moral
+concerns or intuitions (Shweder & Sullivan, 1993; Shweder et al., 1997;
+Graham et al., 2011). Our results support the
+multi-dimensionality of moral judgments and further demonstrate the
+importance of previously identified moral factors, including norm
+violation (Shweder et al.,1997; Haidt, 2007), social affect (Moll
+et al., 2001; Moll et al., 2002), and the intention (Koster-Hale et al.,
+2013). Similar to the American and Iranian
+populations, Russian participants demonstrated that social norms play a
+key role in moral judgments, followed by social emotions, and then
+intentions. Overall, the moral judgments of the participants in our
+study can be explained by their moral intuitions, as suggested by the
+moral foundations theory (Haidt, 2007; Graham et al., 2013). These moral
+intuitions support rapid, effortless, associative, and heuristic
+cognition developed in evolution and varyingly shaped by socio-cultural
+experiences from childhood (Graham et al., 2013).
+
+Our findings are also supported by the literature on moral psychology.
+Social norms and values have been previously reported as influential
+factors in the development of moral psychology and the behavior of
+individuals in different societal environments (Kohlberg, 1963b;
+Schwartz, 1992; Schwartz & Bilsky, 1987; Schwartz & Bilsky, 1990).
+Violation of social norms evokes moral judgment across various
+situations, as demonstrated by the norm violation factor in our
+findings. Similarly, the finding of the social affect factor is also in
+line with the literature on moral cognition and psychology. This
+component includes the event features of emotional aversion, emotional
+intensity, and socialness, and it demonstrates that individuals make
+moral evaluations through cognitive and socioemotional judgment of
+various moral features associated with different events and situations
+encountered in everyday real-life circumstances (Greene & Haidt, 2002;
+Haidt, 2007; Graham et al., 2009). Emotional brain responses have also
+been demonstrated in the functional magnetic resonance imaging (fMRI) of
+a task involving passive visual attention to images of morality-evoking
+scenes (Moll et al., 2002). Our results confirmed the intention as the
+third component for the moral judgment of our participants. This
+component includes the event features of self-benefit and premeditation.
+Intention is generally defined as the instrumentality of the action,
+omission, or the character of the protagonist for self-benefit in moral
+events or situations by premeditation and planning of the protagonist.
+An fMRI study of a task for subjects reading narratives with accidental
+or intentional harms showed a distinct pattern of neural responses for
+accidental versus intentional harms, demonstrating that the
+intentionality of an action is an important component in moral judgment
+and behavior (Koster-Hale et al., 2013). The role of intentionality is
+even more pronounced in the analysis of intuitions in classic moral
+dilemmas, such as trolley dilemmas (Thomson, 1985; Waldmann & Dieterich,
+2007; Waldman & Wiegmann, 2010).
+
+Our ESEM modeling results generally confirmed the factor structure and
+variable loadings identified in the EFA. Three cross-loadings were also
+supported: (i) cross-loading of emotional aversion onto norm violation
+and social affect factors, (ii) cross-loading of self-benefit onto
+intention and social affect, and (iii) cross-loading of harm onto norm
+violation and social affect factors. Overall, these cross-loading
+profiles align with those reported in the American sample (Kruepke et
+al., 2018); however, emotional aversion had a somewhat stronger loading
+on norm violation than on social affect in the Russian sample, whereas
+in the American one, it was initially presumed to contribute more
+strongly to social affect (see **Tables 3** and **5** for details).
+Additionally, the negative cross-loading of self-benefit to social
+affect was more pronounced in the Russian sample, whereas the positive
+cross-loading of harm was stronger in the American sample.
+
+Previous studies indicated that that compared to
+Western Europeans, Eastern Europeans may hold the belief that multiple,
+even opposite, truths are possible (e.g., Peng & Nisbett, 1999; Varnum
+et al., 2008), which can potentially result in differences in moral
+judgments in different cultures. Among other characteristics, Russian
+and American populations also differ in collectivism and power distance.
+According to Hofstede’s model and its revision, the United States (IDV =
+91) is an individualistic country, whereas Russia (IDV = 39) is
+collectivistic. Furthermore, the Hofstede Cultural Dimensions model
+assigns Russia a score of 93 (out of 100) for the power distance, while
+the United States showed a score of 40. A prominent cross-cultural study
+investigated perceptions of the appropriateness of various responses to
+a violation of a cooperative norm and to atypical social behaviors and
+showed that appropriateness ratings of physical confrontation and social
+ostracism were negatively correlated with individualism and positively
+correlated with power distance (Eriksson et al., 2021). These findings
+are in line with the stronger loading of emotional aversion onto the
+Norm Violation factor in the Russian sample (in our study)
+compared to the American sample (Kruepke et al., 2018). The high level
+of collectivism may also explain the stronger negative cross-loading of
+self-benefit (fairness) to Social Affect in the Russian sample.
+
+Based on interpretations from available literature for sociomoral
+reasoning and behavior, we also identified the following latent
+profiles: Peccadillo, Illegal & Antisocial, Controversial Act,
+Prosocial, and Social Conflict. Interestingly, the Social Conflict
+profile was not identified in the original study of the American
+population (Kruepke et al., 2018), yet it consistently emerged in both
+our VEI and VII models as a highly robust category for our Russian
+sample. The cross-cultural differences in collectivism may perhaps
+explain some differences in the latent profiles in the Russian and
+American samples. Notably, our analysis of the Russian population also
+did not yield the Deception profile found in Kruepke et al.’s (2018)
+study of the American population. In our sample, most vignettes
+previously categorized as Deception were absorbed into the Peccadillo
+profile. Collectivistic attitudes have been linked to
+a tolerance of deceptive behavior when it is used to avoid conflict and
+support harmony (Seiter et al. 2002), whereas in individualistic
+cultures, saying the truth is often an important norm (Hall & Whyte,
+2008). Furthermore, Eastern Europeans often report the belief that
+multiple and contradictory truths are possible (e.g., Peng & Nisbett,
+1999; Spencer-Rodgers et al., 2010; Varnum et al., 2008), which may
+affect their views on deception in various social contexts. Future
+studies are clearly needed to explain and replicate the cross-cultural
+differences in some aspects of moral judgments reported in the current
+study.
+
+Our moral study has some limitations due to project timeline
+constraints. The participants’ personality traits, emotional states,
+moral developmental levels, and other nonmoral cognitive functions were
+not assessed as additional measures. Therefore, future studies of
+morality should also include assessment of personality traits to examine
+the differential effects of traits and affective states on moral
+judgment and behavior. It should also be noted that 73% of our study
+participants were female. The statistical analysis did not demonstrate a
+significant effect of gender on moral judgments in our study; however,
+future studies should aim for gender-balanced samples.
+
+**Conclusion**
+
+In two (main and confirmatory) large-scale behavioral studies, we
+further validated a research tool that probes moral judgment using a
+battery of realistic vignettes representing both positive and negative
+moral experiences. Using qualitative and quantitative statistical
+analyses, we observed highly similar responses in the evaluation of the
+vignettes’ event plots and event features by Russian participants
+(current study) compared to American individuals (Knutson et al., 2010;
+Kruepke et al., 2018). Similar to previous studies, our EFA of the event
+feature ratings revealed norm violation, social affect and intention as
+fundamental components of moral judgment. Although the cross-loading
+profiles largely aligned with the previously published American sample
+(Kruepke et al., 2018), emotional aversion had a stronger loading on
+norm violation in the Russian sample than in the American sample.
+Notably, our LPA of the Russian sample did not yield the Deception
+profile found in Kruepke et al.’s (2018) study of the American
+population, whereas Social Conflict emerged as a highly robust category
+only in our Russian sample. Thus, our results call for additional
+cross-cultural studies for a more detailed validation of the realistic
+moral vignettes used in studies of moral judgments. Overall, our
+findings demonstrate the feasibility and reliability of the previously
+published vignettes (Knutson et al., 2010; Kruepke et al., 2018)
+narrating real-life moral scenarios for the assessment of moral
+judgment. The set of vignettes provides investigators with effective
+tools to ensure broad coverage of the key factors implicated in moral
+judgments in different cultures.
+
+# Declarations
+
+## Funding
+
+This article is an output of a research project
+implemented as part of the Basic Research Program at the National
+Research University Higher School of Economics (HSE University).
+
+## Conflicts of interest
+
+The authors declared that they had no conflict of
+interest with respect to their authorship or the publication of this
+article.
+
+## Ethics approval
+
+The study was conducted in accordance with the Declaration of Helsinki
+and approved by the Ethics Committee of the HSE University.
+
+## Consent to participate
+
+All participants were provided with brief instructions and gave informed
+consent before participating in this study.
+
+## Consent for publication
+
+Not applicable.
+
+## Open practices statement
+
+Supplemental Material and data presented in this
+study, as well as minimal code to reproduce the main findings,
+are openly and freely available online via the Open
+Science Framework (OSF):
+[https://osf.io/9k5b8/](https://osf.io/9k5b8/). None of
+the experiments in this study were preregistered.
+
+## Authors’ contributions
+
+Studies 1 and 2 were supervised by Isak B. Blank and Vasily Klucharev.
+Study 1 was established and planned by Isak B. Blank, Vasily Klucharev,
+and Rustam Asgarov. Study 2 was established and planned by Isak B.
+Blank, Vasily Klucharev, and Zorina Rakhmankulova. Rustam Asgarov
+prepared the Google questionnaire and collected the data for Study 1,
+while Zorina Rakhmankulova performed these tasks for Study 2. Zorina
+Rakhmankulova and Rustam Asgarov conducted the statistical analyses and
+prepared the relevant scripts for Study 1, whereas Zorina Rakhmankulova
+and Semyon Mening performed these tasks for Study 2. Zorina
+Rakhmankulova and Semyon Mening processed the experimental data for both
+studies and generated all final scripts, tables, figures, and
+supplementary materials. The initial manuscript was drafted by Zorina
+Rakhmankulova and Rustam Asgarov. The final manuscript was reviewed,
+edited, and written by Zorina Rakhmankulova, Eliana Monahhova, and
+Vasily Klucharev. All authors reviewed and approved the final
+manuscript.
+
+## Acknowledgements
+
+We express our gratitude to Anna Tokmovtseva from the Russian Orthodox
+University of Saint John the Divine for her invaluable assistance in
+calculating the reading ease index for the Russian-language version of
+the vignettes and preparing the supplemental materials. We also thank
+our colleagues Anna Shepelenko, Nina Kazanina, Ksenia Panidi, and Anna
+Shestakova from the Institute for Cognitive Neuroscience of the HSE
+University for their valuable help with the Russian-language translation
+of the vignettes and event feature scales, as well as their assistance
+with the recruitment of the participants.
+
+# References
+
+Asparouhov, T., & Muthén, B. (2009). Exploratory
+structural equation modeling. *Structural equation modeling: A
+Multidisciplinary Journal*, *16*(3), 397–438.
+[https://doi.org/10.1080/10705510903008204](https://doi.org/10.1080/10705510903008204)
+
+Axelrod, R., & Hamilton, W. D. (1981). The evolution of cooperation.
+*Science*, *211*(4489), 1390–1396.
+[https://doi.org/10.1126/science.7466396](https://doi.org/10.1126/science.7466396)
+
+Bartlett, M.S. (1951). The effect of standardization
+on a χ2 approximation in factor analysis, *Biometrika*,
+*38*(3-4), 337–344.
+[https://doi.org/10.2307/2332580](https://doi.org/10.2307/2332580)
+
+Brown, T. A. (2015). *Confirmatory factor analysis for applied research*
+(2nd ed.). The Guilford Press.
+
+Burt, C. (1948). The factorial study of temperament
+traits. *British Journal of Psychology*, *1*, 178–203.
+[https://doi.org/10.1007/BF02288799](https://doi.org/10.1007/BF02288799)
+
+Cattell, R. B. (1966). The scree test for the number of factors.
+*Multivariate Behavioral Research*, *1*(2), 245–276.
+[https://doi.org/10.1207/s15327906mbr0102_10](https://doi.org/10.1207/s15327906mbr0102_10)
+
+Cialdini, R. B., Reno, R. R., & Kallgren, C. A. (1990). A focus theory
+of normative conduct: Recycling the concept of norms to reduce littering
+in public places. *Journal of Personality and Social Psychology*,
+*58*(6), 1015–1026.
+[https://doi.org/10.1037/0022-3514.58.6.1015](https://doi.org/10.1037/0022-3514.58.6.1015)
+
+Crawford, A. V., Green, S. B., Levy, R., Lo, W. L., Scott, L., Svetina,
+D., & Thompson, M. S. (2010). Evaluation of parallel analysis methods
+for determining the number of factors. *Educational and Psychological
+Measurement*, *70*(6), 885–901.
+[https://doi.org/10.1177/0013164410379332](https://doi.org/10.1177/0013164410379332)
+
+Dunn, A. M., Heggestad, E. D., Shanock, L. R., & Theilgard, N. (2018).
+Intra-individual response variability as an indicator of insufficient
+effort responding: Comparison to other indicators and relationships with
+individual differences. *Journal of Business and Psychology*, *33*,
+105-121.
+[https://doi.org/10.1007/s10869-016-9479-0](https://doi.org/10.1007/s10869-016-9479-0)
+
+Eriksson, K., Strimling, P., Gelfand, M., Wu, J., Abernathy, J., Akotia,
+C. S., Aldashev, A., Ananyeva, K. I., Andersson, P. A., Arikan, G.,
+Batanova, M., Becker, M., Birney, M. E., Boehnke, K., Bortolini, T.,
+Choi, H., Chu, Q., Chuang, S., Collins, E., ... Van Lange, P. A. M.
+(2021). Perceptions of the appropriate response to norm violation in 57
+societies. *Nature Communications*, *12*(1), 1481.
+[https://doi.org/10.1038/s41467-021-21602-9](https://doi.org/10.1038/s41467-021-21602-9)
+
+Escobedo, J. R. (2009). *Investigating moral events: Characterization
+and structure of autobiographical moral memories* \[Doctoral
+dissertation, California Institute of Technology\].
+[https://resolver.caltech.edu/CaltechETD:etd-11112008-122002](https://resolver.caltech.edu/CaltechETD:etd-11112008-122002)
+
+Fehr, E., & Fischbacher, U. (2003). The nature of human altruism.
+*Nature*, *425*(6960), 785–791.
+[https://doi.org/10.1038/nature02043](https://doi.org/10.1038/nature02043)
+
+Flesch, R. (1948). A new readability yardstick. *Journal of Applied
+Psychology*, *32*, 221–233.
+[https://doi.org/10.1037/h0057532](https://psycnet.apa.org/doi/10.1037/h0057532)
+
+Fraley, C., & Raftery, A. E. (1998). How many clusters? Which clustering
+method? Answers via model-based cluster analysis. *The Computer
+Journal*, *41*(8), 578–588.
+[https://doi.org/10.1093/comjnl/41.8.578](https://doi.org/10.1093/comjnl/41.8.578)
+
+Fraley, C., & Raftery, A. E. (2002). Model-based clustering,
+discriminant analysis, and density estimation. *Journal of the American
+Statistical Association*, *97*(458), 611–631.
+[https://doi.org/10.1198/016214502760047131](https://doi.org/10.1198/016214502760047131)
+
+Graham, J., Haidt, J., & Nosek, B. A. (2009). Liberals and conservatives
+rely on different sets of moral foundations. *Journal of Personality and
+Social Psychology*, *96*(5), 1029–1046.
+[https://doi.org/10.1037/a0015141](https://doi.org/10.1037/a0015141)
+
+Graham, J., Haidt, J., Koleva, S., Motyl, M., Iyer, R., Wojcik, S. P., &
+Ditto, P. H. (2013). Moral foundations theory: The pragmatic validity of
+moral pluralism. In P. G. Devine & A. Plant (Eds.), *Advances in
+experimental social psychology* (Vol. 47, pp. 55–130). Academic Press.
+[https://doi.org/10.1016/B978-0-12-407236-7.00002-4](https://doi.org/10.1016/B978-0-12-407236-7.00002-4)
+
+Graham, J., Meindl, P., Beall, E., Johnson, K. M., & Zhang, L. (2016).
+Cultural differences in moral judgment and behavior, across and within
+societies. *Current Opinion in Psychology*, *8*, 125–130.
+[https://doi.org/10.1016/j.copsyc.2015.09.007](https://doi.org/10.1016/j.copsyc.2015.09.007)
+
+Graham, J., Meyer, L. H., McKenzie, L., McClure, J., & Weir, K. F.
+(2010). Maori and Pacific secondary student and parent perspectives on
+achievement, motivation and NCEA. *Assessment Matters*, *2*, 132–157.
+
+
+Graham, J., Nosek, B. A., Haidt, J., Iyer, R., Koleva, S., & Ditto, P.
+H. (2011). Mapping the moral domain. *Journal of Personality and Social
+Psychology*, *101*(2), 366–385.
+[https://doi.org/10.1037/a0021847](https://doi.org/10.1037/a0021847)
+
+Greene, J. D., Sommerville, R. B., Nystrom, L. E., Darley, J. M., &
+Cohen, J. D. (2001). An fMRI investigation of emotional engagement in
+moral judgment. *Science*, *293*(5537), 2105–2108.
+[https://doi.org/10.1126/science.1062872](https://doi.org/10.1126/science.1062872)
+
+Greene, J., & Haidt, J. (2002). How (and where) does moral judgment
+work?. *Trends in Cognitive Sciences*, *6*(12), 517–523.
+[https://doi.org/10.1016/S1364-6613(02)02011-9](https://doi.org/10.1016/S1364-6613(02)02011-9)
+
+Guerra, V. M., & Giner-Sorolla, R. (2010). The Community, Autonomy, and
+Divinity Scale (CADS): A new tool for the cross-cultural study of
+morality. *Journal of Cross-Cultural Psychology*, *41*(1), 35–50.
+[https://doi.org/10.1177/0022022109348919](https://doi.org/10.1177/0022022109348919)
+
+Guttman, L. (1954). Some necessary conditions for common-factor
+analysis. *Psychometrika*, *19*(2), 149–161.
+[https://doi.org/10.1007/BF02289162](https://doi.org/10.1007/BF02289162)
+
+Haidt, J. (2001). The emotional dog and its rational tail: A social
+intuitionist approach to moral judgment. *Psychological Review*,
+*108*(4), 814–834.
+[https://doi.org/10.1037/0033-295X.108.4.814](https://doi.org/10.1037/0033-295X.108.4.814)
+
+Haidt, J. (2007). The new synthesis in moral psychology. *Science*,
+*316*(5827), 998–1002.
+[https://doi.org/10.1126/science.1137651](https://doi.org/10.1126/science.1137651)
+
+Haidt, J. (2012). *The righteous mind: Why good people are divided by
+politics and religion*. New York Pantheon.
+
+Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010).
+*Multivariate data analysis* (7th ed.). Prentice Hall.
+
+Hall, E. T., & Whyte, W. F. (2008). Intercultural communication. In C.
+D. Mortensen (Ed.), *Communication theory* (2nd ed., pp. 17–33).
+Routledge.
+[https://doi.org/10.4324/9781315080918-32](https://doi.org/10.4324/9781315080918-32)
+
+Hawley, P. H. (2003). Strategies of control, aggression, and morality in
+preschoolers: An evolutionary perspective. *Journal of Experimental
+Child Psychology*, *85*, 213–235.
+[https://doi.org/10.1016/s0022-0965(03)00073-0](https://doi.org/10.1016/s0022-0965(03)00073-0)
+
+Heekeren, H. R., Wartenburger, I., Schmidt, H., Schwintowski, H. P., &
+Villringer, A. (2003). An fMRI study of simple ethical decision-making.
+*Neuroreport*, *14*(9), 1215–1219.
+[https://doi.org/10.1097/00001756-200307010-00005](https://doi.org/10.1097/00001756-200307010-00005)
+
+Hoyle, R. H. (1995). *Structural equation modeling: Concepts, issues,
+and applications*. Sage Publications.
+
+Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in
+covariance structure analysis: Conventional criteria versus new
+alternatives. *Structural Equation Modeling: A Multidisciplinary
+Journal*, *6*(1), 1–55.
+[https://doi.org/10.1080/10705519909540118](https://doi.org/10.1080/10705519909540118)
+
+Kaiser, H. F. (1958). The varimax criterion for analytic rotation in
+factor analysis. *Psychometrika*, *23*(3), 187–200.
+[https://doi.org/10.1007/BF02289233](https://doi.org/10.1007/BF02289233)
+
+Kaiser, H. F. (1960). The application of electronic computers to factor
+analysis. *Educational and Psychological Measurement*, *20*(1), 141–151.
+[https://doi.org/10.1177/001316446002000116](https://doi.org/10.1177/001316446002000116)
+
+Kaiser, H. F., & Rice, J. (1974). Little Jiffy, Mark IV. *Journal of
+Educational and Psychological Measurement*, *34*(1), 111–117.
+[https://doi.org/10.1177/001316447403400115](https://doi.org/10.1177/001316447403400115)
+
+Kincaid, J. P., Fishburne, R. P., Jr., Rogers, R. L., & Chissom, B. S.
+(1975). *Derivation of new readability formulas (Automated Readability
+Index, Fog Count and Flesch Reading Ease Formula) for Navy enlisted
+personnel* (Research Branch Report No. 8-75). Naval Technical Training
+Command, Research Branch.
+[https://doi.org/10.21236/ADA006655](https://doi.org/10.21236/ADA006655)
+
+Kline, R. B. (2016). *Principles and practice of structural equation
+modeling* (4th ed.). Guilford Press.
+
+Knutson, K. M., Krueger, F., Koenigs, M., Hawley, A., Escobedo, J. R.,
+Vasudeva, V., *et al*. (2010). Behavioral norms for condensed moral
+vignettes. *Social Cognitive and Affective Neuroscience*, *5*(4),
+378–384.
+[https://doi.org/10.1093/scan/nsq005](https://doi.org/10.1093/scan/nsq005)
+
+Kohlberg, L. (1963b). Moral development and identification. *Teachers
+College Record*, *64*(9), 277–332.
+[https://doi.org/10.1037/13101-008](https://doi.org/10.1037/13101-008)
+
+Koster-Hale, J., Saxe, R., Dungan, J., & Young, L. L. (2013). Decoding
+moral judgments from neural representations of intentions. *Proceedings
+of the National Academy of Sciences*, *110*(14), 5648–5653.
+[https://doi.org/10.1073/pnas.1207992110](https://doi.org/10.1073/pnas.1207992110)
+
+Kruepke, M., Molloy, E. K., Bresin, K., Barbey, A. K., & Verona, E.
+(2018). A brief assessment tool for investigating facets of moral
+judgment from realistic vignettes. *Behavior Research Methods*, *50*(3),
+922–936.
+[https://doi.org/10.3758/s13428-017-0917-3](https://doi.org/10.3758/s13428-017-0917-3)
+
+Lim, S., & Jahng, S. (2019). Determining the number of factors using
+parallel analysis and its recent variants. *Psychological Methods*,
+*24*(4), 452–467.
+[https://doi.org/10.1037/met0000230](https://doi.org/10.1037/met0000230)
+
+Lorenzo-Seva, U., & Ten Berge, J. M. (2006). Tucker's congruence
+coefficient as a meaningful index of factor similarity. *Methodology*,
+*2*(2), 57–64.
+[https://doi.org/10.1027/1614-2241.2.2.57](https://doi.org/10.1027/1614-2241.2.2.57)
+
+Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis
+with applications. *Biometrika*, *57*(3), 519–530.
+[https://doi.org/10.1093/biomet/57.3.519](https://doi.org/10.1093/biomet/57.3.519)
+
+Marsh, H. W., Lüdtke, O., Muthén, B., Asparouhov, T., Morin, A. J. S.,
+Trautwein, U., & Nagengast, B. (2010). A new look at the big five factor
+structure through exploratory structural equation modeling.
+*Psychological Assessment*, *22*(3), 471–491.
+[https://doi.org/10.1037/a0019227](https://doi.org/10.1037/a0019227)
+
+Marsh, H. W., Lüdtke, O., Nagengast, B., Morin, A. J., & Von Davier, M.
+(2013). Why item parcels are (almost) never appropriate: Two wrongs do
+not make a right—camouflaging misspecification with item parcels in CFA
+models. *Psychological Methods*, *18*(3), 257–284.
+[https://doi.org/10.1037/a0032773](https://doi.org/10.1037/a0032773)
+
+Marsh, H. W., Morin, A. J., Parker, P. D., & Kaur, G. (2014).
+Exploratory structural equation modeling: An integration of the best
+features of exploratory and confirmatory factor analysis. *Annual Review
+of Clinical Psychology*, *10*, 85–110.
+[https://doi.org/10.1146/annurev-clinpsy-032813-153700](https://doi.org/10.1146/annurev-clinpsy-032813-153700)
+
+McDonald, R. P. (1985). *Factor analysis and related methods*. Lawrence
+Erlbaum Associates.
+[https://doi.org/10.4324/9781315802510](https://doi.org/10.4324/9781315802510)
+
+Moll, J., Eslinger, P. J., & Oliveira-Souza, R. D. (2001). Frontopolar
+and anterior temporal cortex activation in a moral judgment task:
+Preliminary functional MRI results in normal subjects. *Arquivos de
+Neuro-psiquiatria*, *59*, 65–664.
+[https://doi.org/10.1590/S0004-282X2001000500001](https://doi.org/10.1590/S0004-282X2001000500001)
+
+Moll, J., de Oliveira-Souza, R., Eslinger, P. J., Bramati, I. E.,
+Mourao-Miranda, J., Andreiuolo, P. A., & Pessoa, L. (2002). The neural
+correlates of moral sensitivity: A functional magnetic resonance imaging
+investigation of basic and moral emotions. *Journal of Neuroscience*,
+*22*(7), 2730–2736.
+[https://doi.org/10.1523/JNEUROSCI.22-07-02730.2002](https://doi.org/10.1523/JNEUROSCI.22-07-02730.2002)
+
+Morin, A.J.S., Marsh, H.W., & Nagengast, B. (2013). Exploratory
+structural equation modeling. In G.R. Hancock & R.O. Mueller (Eds.),
+*Structural Equation Modeling: A Second Course* (pp.
+395–436). Information Age Publishing.
+
+Oborneva, I. V. (2006). *Automated assessment of the complexities of
+educational texts based on statistical parameters* (Unpublished doctoral
+dissertation).
+
+Peng, K., & Nisbett, R. E. (1999). Culture, dialectics, and reasoning
+about contradiction. *American Psychologist*, *54*(9), 741-754.
+[https://doi.org/10.1037/0003-066X.54.9.741](https://doi.org/10.1037/0003-066X.54.9.741)
+
+R Core Team (2022). *R: A language and environment for statistical
+computing* \[Computer software\]. R Foundation for Statistical
+Computing.
+[https://www.R-project.org/](https://www.r-project.org/)
+
+Revelle, W. (2023). *psych: Procedures for psychological, psychometric,
+and personality research* (R package version 2.3.6) \[Computer
+software\]. Northwestern University.
+[https://CRAN.R-project.org/package=psych](https://cran.r-project.org/package=psych)
+
+Rosseel, Y. (2012). lavaan: An R Package for Structural Equation
+Modeling. *Journal of Statistical Software*, *48*(2), 1–36.
+[https://doi.org/10.18637/jss.v048.i02](https://doi.org/10.18637/jss.v048.i02)
+
+Rusbult, C. E., & Van Lange, P. A. M. (1996). Interdependence processes.
+In E. T. Higgins & A. W. Kruglanski (Eds.), *Social psychology: Handbook
+of basic principles* (pp. 564–596). Guilford Press.
+
+Satorra, A., & Bentler, P. M. (2001). A scaled difference chi-square
+test statistic for moment structure analysis. *Psychometrika*, *66*,
+507–514.
+[https://doi.org/10.1007/BF02296192](https://doi.org/10.1007/BF02296192)
+
+Schwartz, S. H. (1977). Normative influences on altruism. *Advances in
+Experimental Social Psychology*, *10*, 221–279.
+[https://doi.org/10.1016/S0065-2601(08)60358-5](https://doi.org/10.1016/S0065-2601(08)60358-5)
+
+Schwartz, S. H. (1992). Universals in the content and structure of
+values: Theoretical advances and empirical tests in 20 countries. In M.
+P. Zanna (Ed.), *Advances in experimental social psychology* (Vol. 25,
+pp. 1–65). Academic Press.
+[https://doi.org/10.1016/S0065-2601(08)60281-6](https://doi.org/10.1016/S0065-2601(08)60281-6)
+
+Schwartz, S. H., & Bilsky, W. (1987). Toward a universal psychological
+structure of human values. *Journal of Personality and Social
+Psychology*, *53*(3), 550–562.
+[https://doi.org/10.1037/0022-3514.53.3.550](https://doi.org/10.1037/0022-3514.53.3.550)
+
+Schwartz, S. H., & Bilsky, W. (1990). Toward a theory of the universal
+content and structure of values: Extensions and cross-cultural
+replications. *Journal of Personality and Social Psychology*, *58*(5),
+878–891.
+[https://doi.org/10.1037/0022-3514.58.5.878](https://doi.org/10.1037/0022-3514.58.5.878)
+
+Schwarz, G. (1978). Estimating the dimension of a model. *The Annals of
+Statistics*, *6*(2), 461–464.
+[https://doi.org/10.1214%2Faos%2F1176344136](https://doi.org/10.1214%2Faos%2F1176344136)
+
+Seiter, J. S., Bruschke, J., & Bai, C. (2002). The acceptability of
+deception as a function of perceivers' culture, deceiver's intention,
+and deceiver‐deceived relationship. *Western Journal of Communication*,
+*66*(2), 158–180.
+[https://doi.org/10.1080/10570310209374731](https://doi.org/10.1080/10570310209374731)
+
+Shweder, R. A., & Sullivan, M. A. (1993). Cultural psychology: Who needs
+it? *Annual Review of Psychology*, *44*(1), 497–523.
+[https://doi.org/10.1146/annurev.ps.44.020193.002433](https://doi.org/10.1146/annurev.ps.44.020193.002433)
+
+Shweder, R. A., Much, N. C., Mahapatra, M., & Park, L. (1997). The "big
+three" of morality (autonomy, community, divinity) and the "big three"
+explanations of suffering. In A. M. Brandt & P. Rozin (Eds.), *Morality
+and health* (pp. 119–169). Routledge.
+
+Silvestrin, M. T., & de Beer, L. (2022). *esemComp: ESEM-withinCFA
+syntax composer* (R package version 0.2) \[Computer software\].
+[https://mateuspsi.github.io/esemComp](https://mateuspsi.github.io/esemComp)
+
+Spencer-Rodgers, J., Williams, M. J., & Peng, K. (2010). Cultural
+differences in expectations of change and tolerance for contradiction: A
+decade of empirical research. *Personality and Social Psychology
+Review*, *14*(3), 296–312.
+[https://doi.org/10.1177/1088868310362982](https://doi.org/10.1177/1088868310362982)
+
+Spooren, P., Mortelmans, D., & Van Loon, F. (2012). Exploratory
+Structural Equation Modelling (ESEM): Application to the SET-37
+questionnaire for students' evaluation of teaching. *Procedia-Social and
+Behavioral Sciences*, *69*, 1282–1288.
+[https://doi.org/10.1016/j.sbspro.2012.12.063](https://doi.org/10.1016/j.sbspro.2012.12.063)
+
+Steiner, M., & Grieder, S. (2020). EFAtools: An R package with fast and
+flexible implementations of exploratory factor analysis tools. *Journal
+of Open Source Software*, *5*(53), 2521.
+[https://doi.org/10.21105/joss.02521](https://doi.org/10.21105/joss.02521)
+
+Thomson, J. J. (1985). The Trolley Problem. *The Yale Law Journal*,
+*94*(6), 1395–1415.
+[https://doi.org/10.2307/796133](https://doi.org/10.2307/796133)
+
+Tower, R. K., Kelly, C., & Richards, A. (1997). Individualism,
+collectivism and reward allocation: A cross‐cultural study in Russia and
+Britain. *British Journal of Social Psychology*, *36*(3), 331–345.
+[https://doi.org/10.1111/j.2044-8309.1997.tb01135.x](https://doi.org/10.1111/j.2044-8309.1997.tb01135.x)
+
+Tucker, L. R. (1951). *A method for synthesis of factor analysis
+studies* (Personnel Research Section Report No. 984). Department of the
+Army.
+
+Tyler, T. R. (2006). *Why people obey the law*. Princeton University
+Press.
+
+Ulitzsch, E., Pohl, S., Khorramdel, L., Kroehne, U., & von Davier, M.
+(2022). A response-time-based latent response mixture model for
+identifying and modeling careless and insufficient effort responding in
+survey data. *Psychometrika*, *87*(2), 593–619.
+[https://doi.org/10.3102/10769986231173607](https://doi.org/10.3102/10769986231173607)
+
+Varnum, M., Grossmann, I., Katunar, D., Nisbett, R., & Kitayama, S.
+(2008). Holism in a European cultural context: Differences in cognitive
+style between Central and East Europeans and Westerners. *Journal of
+Cognition and Culture*, *8*(3-4), 321–333.
+[https://doi.org/10.1163/156853708X358209](https://doi.org/10.1163/156853708X358209)
+
+Waldmann, M. R., & Dieterich, J. H. (2007). Throwing a bomb on a person
+versus throwing a person on a bomb: Intervention myopia in moral
+intuitions. *Psychological Science*, *18*(3), 247–253.
+[https://doi.org/10.1111/j.1467-9280.2007.01884.x](https://doi.org/10.1111/j.1467-9280.2007.01884.x)
+
+Waldmann, M. R., & Wiegmann, A. (2010). A double causal contrast theory
+of moral intuitions in trolley dilemmas. *Proceedings of the Annual
+Meeting of the Cognitive Science Society*, *32*, 2589–2594.
+[https://escholarship.org/uc/item/1vc6w370](https://escholarship.org/uc/item/1vc6w370)
+
+Weston, R., & Gore, P. A. (2006). A brief guide to structural equation
+modeling. *The Counseling Psychologist*, *34*(5), 719–751.
+[https://doi.org/10.1177/0011000006286345](https://doi.org/10.1177/0011000006286345)
+
+Yazdanpanah, A., Soltani, S., Mirfazeli, F. S., Shariat, S. V.,
+Jahanbakhshi, A., GhaffariHosseini, F., Rezaei, Z., & Grafman, J.
+(2021). Iranian and American Moral Judgments for Everyday Dilemmas Are
+Mostly Similar. *Frontiers in Psychology*, *12*, 640620.
+[https://doi.org/10.3389/fpsyg.2021.640620](https://doi.org/10.3389/fpsyg.2021.640620)
+
+Young, L., Cushman, F., Hauser, M., & Saxe, R. (2007). The neural basis
+of the interaction between theory of mind and moral judgment.
+*Proceedings of the National Academy of Sciences*, *104*(20), 8235–8240.
+[https://doi.org/10.1073/pnas.0701408104](https://doi.org/10.1073/pnas.0701408104)
+
+Yuan, K. H., & Bentler, P. M. (2000). Three likelihood-based methods for
+mean and covariance structure analysis with nonnormal missing data.
+*Sociological Methodology*, *30*(1), 165–200.
+[https://doi.org/10.1111/0081-1750.00078](https://doi.org/10.1111/0081-1750.00078)
diff --git a/docs/mfv_russia/media/image1.png b/docs/mfv_russia/media/image1.png
new file mode 100644
index 0000000000000000000000000000000000000000..6faa497b73ac6c107c7fbda4f28a982f2027bb1c
GIT binary patch
literal 178473
zcmeFZWmJ`27d4ECN+=)*3P_27(uj16ND4?d0!nu`9;GBiK|)CdMY=;$Nd-v>X=$WG
zy54npm(O_Me&1i;_;UQ!p2k5VN+hwo|gvH@q)#-|D`dsiC684Ky^KkPsCelZz_n
zgks+3GhS|a)cN*p816pj^8yRScAviK{rmZr15Du-1)@(s&ke?z(s7LKCYxRrIwy+%
z@!j|E?+02R>8Z`q=YyDE7bv{Ocywj6ZCm11$9b&T_Z<0U;U(r3O#@q#v5Q_)L-q?p
zI8RHMv?7OS(V0lIm?yrD<~K~%*77-g^lUrh*~ZNHB!VG=?ikrvbyeT|B31oR&wBC3
zf$?mXg2b_aLEmo~JdKw|@1yM#)#d3tz!2T}gwyRy^AAE)pL!ukC*%
zADYkRd2aZ4$$AunUF3*AeuB#QfI~nwi)nrx{=xnCrdZ1~XgX!(zINElLbZk}?&TM!
z9a7V{+dnX-biOdgAAaQG_)(y-e&ObBA-i5IwxgF~?CD873bgN|49q!h8#EL-w@n!W
znnKh}xEfXhHcKXnjnF+8n2k;OdfIs37zK(lEd-6NoK;N`s#QykkGip^$6S1_o>tIL
zH}4ZW!W%#_dP%Tk($Y+s{KCW{_Y-LVbJ$cCnqU5ZsI?9t}MOX1^eB-BY%
zVf3Gs?gig#=aa_eY>2#Me{c1Dd#CF$8pQ6S(oewSH?g
zZ;a@%(&h8CQVS}(rJ7LIZU4jJvN5R{v%{yp&N#c)gKE90o*ca?Zq3vCr@W4P&-SYp
zbIxA>qKfTVYOPf(u_`EcPv{M^iiLiD(rXP))opajCN$6YE@ha;C$)CRH$Dr8nSb60
zqong58yXiHX|YeRORyb&@JZ)8i=CdGo-L()f`nP8>*96uh3uqU>`Bw8I|B(fG`}$m
zT)F2fSY17E%6&ZpYXS!LvRj|eN@iabg0
z@pf#HDn_am}w3frzbunUYRYOF1y(N7Pm0UWCAV6{TffT
zwbcIJ`F__o_w5r-^6b*{Z)g_r;(gCJ-nL`X#Pz^wB8-|W&ira
z%a|3%|Lym7m#IXflRgtO{L5E{C0RUTO@DpN|CZ#x;_ZJ+^1mg4{P4en^Iy5=|Dz?T
zus@fsfOYTI_h#N(#B^H9f6`L!oWRP9u-9#t?qB((SX;3>$5$qrzr$VOvobs>udfQ{vthC+IF95
zU&!M385GP8VF?ocOTvH0e+;X-G=|{#rGKd%ZH`bnbd1xD|LcJLmcbqAzn}i&ofP6f
zXqsmdEmR%suaKIHA62s)q`yx@>EZg5JE^B^zl(~Y#hkrQ7yL-X7hTZpCnlLij;OD*
zsoYV2J+AYCILX!fc&+?VufV5z$_QUh`*!hNtiMXre=q$C3lyUbZjs%imJ_PbdxED_
zU%DJ{B9E>}REw}g|9hMx7V&?p^S{_R^z8iq?qp5JrJJ0Pc_1=*V#swWF2`o1dTBDo
z+V71_l;8f|j{mXaCw;v3w(kpZ9XleWpfoWh@w;#Ia$w?5KmAbdx>_x~zZ|;s;W(a8
zs#1!d>DRZu#tEKW*SWciXS1z`EA_`eJ=0%p=Eh5s2*q`u%a!2729TUU9OOur7QUYTeryMNPu@(WhN?l?Wp6k&bxt0TXkID9`=
z4rV!Y$4fSQYd!vPPJLHpW4JO|ZaF|PKUc&Z$LDCK6Wl;`)MKdONa8yc=ImqPyQqC1
zux~w+u=T~b8uoFBIo+Rie>LI2N5^@Pb9ZO>Aj^N`!tvkg?(ZpgS`h++WulAs7*KNx|&v;>Pugy;qLIex%r{YV=Vve0X92B6;uqE+Tm6Y@_FfU`rdan15
zbjYyY&PY$Gt_wQv{|HZrM>d-6RlMJQrr$sJxA#g+gF>h>(MfU)Jsn%ybFFK{`(VRc
zjXA4-@jSEUx%FZ112)g?pQYqUsG}aC1x}FDt=0+*%yUF87M+^|y`y9t^|~
zn9*O(U|>EcaM>L3uaWoO{;Jq~?asj)R?n+UdX)l%hMbZ|9BqXsK(KkG`|zn0o{0!J
zFU2_zrB27YSqeDLblUXZkLsFT3O*_fm6@nS)!AQ=Vd7t*v)LGQ_Fo=rIBy>IzLawJ
z`zig;VYDyEijN%u22qzGaxN3y$G~(@|6ab@GP_i<<~BC)-oD1MM@KEYhJ)p(2SyYD
zacxpUDh1iQE90L>_IDOpyD#TEs%hJb8#P#cKO#=<-iHs2;PkltKqLjjYb{;Rac1AG
z(zWsV)sfr=iu5UJQnDk0{E7^04ijAnd`;T6fq{!w<;(T3ygJ@+)b6?Mo;(T=zJ7r5
zYU2SKDh=~GC#UaJf_Ei@XO}s>^Pr~Br^8^^o84N4%WwrwCk?*x0#;w?u($#%c-xwg+9WYNh>a-xSPN0Bl3bgqQ~Bf31G
zA@sX@i{5xLQ7rQ->{a{QvpW010z%*Ci-&PJj9b2lPk()TyGcCu;pM~YK|N7Uoyk~>
zrO_pKNF8Qj7r&gAJb8*#&Aa}xK1CJ@htW8-Ec+3UaU8N5*V4EVZ9z(c7ayY-XUjR%
z2^`|&&g;6lD(^zLQ!X9KI{r$^ju#ex{`fj;qwJ^r_(P0!rH0h1Y;m(v#*6qXkLVA7
zT4e|-Wm2L-f}DprsvSBTvBg7f>9c&<`SGjg%;Vb0a1W0uWvN8xi0$?>C|n$YwN9nS
z8x&9`^=C7So#d`U>7vQBq2kE4*v!)rfzSxD+3+p{-WIL^bK8>JL_g(r*qt;$r=8TSs~2sD({
z*{4)<#7=l2=p060Bz>j_%bcx>(q%C|nT*7{FT7}f79~7aB5em1oAux%^AX=dgd7f2
z_ek>ieM!^)AY3voCJ0J;*3)MQr{3L3s(wl>MoJf-EV2&BA=v=(=b;Znr36dy;$>Dn
zsfFglw)NM&aZ^d5LVRV9SPjQRDD+4$ULN{q4@8bbE#&%e*@)Ap0Aqw$%i>wrQ#;`W
z!za9N_0J3)>~Hw98`fX9@IXHT&Fy0TY>|I=DU%*mJnY5)!@m3EPZMl?W@
zXdYVw7r;%`v)NS?#8943p1yz&;dP-K&<1|1U%%JRd=062#sIY0S#+vOkMLAa8B$aGKiUP=%PlBQ{-jl*`V1v47b@
zDP#Fsw^llr<%Xv~u?@%{I1&`D_rB@-llOHoz6<%FVI0?GgH;{EJ;ZYn;Bn!xpXZLK
zL~V7j#yY1ncJ84H=EeQ-C@GNA&r&v*XR}zbK{p($Sn9F0!sfNRSuG)pAMdg9SwFv-
zOU5*_BppL&cLdKL&vROA1a*pHI!&Upd>BN3j*Ox{GfwZUQ-|j_G*lJ{=5yF8o{{Gy
zW$^3g#e~;Vb6*pO&UP5r+Mkl=`;pAz>DO{pU^2r2H&30{Sj3i1
z5ZW8eR@)za6Q!%U)i38s9=89nR@x3w8%?}-7A`NAGQ*z3yJx3v8h
zd7tSl`@X#3pl8x8FS)Hz;RValf9{LnYqln}Pzu3nA~o$h;m-6;a`i{3FuerRFEU3+
zEt4>lGpU$}h94X|&LG!4;5M5%veLxL+EuvCfD;Q92;n!`cG-pV3k6^0a10xsWX_8T
zh8%^xavu==+QxNs+lcRE*5&P$mtNgV&n&{GJlJdy+b#cilFWy*Tb+hUtB6TpXD(mA
zKA1!w6ta;Ke@a|aMgpy}V5#b7G!lM$>;2h9W@#W<=VXp1>I^W$HAsgPjbpcW)|LZPQtxSa>&GW6K
zyUo^Kgrn>6%dwROj8@#Gy+$#k7~5nTVqurW|LXjwAi=R0nc
zAmmxPF6wr889RnPNOd=;-Abf&hbG99m9*7^e=^z-TkByF9h!`5UH%E-y&v-avsq;<
z7Vjq_Q=1J}*yz%=3yA_&T}OP+cdx&SbLn#*T8+2q(Q)>r7V~p>eZKgp5Z)#Nv^xCW
z^xHE*sC=opaW12esk1QW2Fq6JGGi?Aq)J8u841+Li-eZ+4z|^htT9_G)Fb&ln4<-A
zcz2T9{Aa-vVWZ5#ZftLhG-5z>Fn^rSaO<-)La%TYt
z@`w4q5rHlY2EDf`g%Yz@ve9WIo}6AzO3%tH>SMU*@*OqugnvQOqqYW5_~H=cIowP|
z**OLMUi6eWC(R4jO%$kkcY*pxdg`fh2TRQi@2?l@f6LL=*}JlLc)^8w1C(@b{VLmo
zEPS3H$>G&Nb18Mb_tu;St02ee13LDN2%2Z$*T5%~rQB}w=j)Zh#5vD$8c%5RZj6^|
zhI;$tPKK8~aE9}9V^j3;F0cD*0u~!-qqGUO*XR4^4cXC-$bAYpkZLurxZi3%fu!)F
zL8pE;tAUbQW8w90hB{$X!BW|q?Y)fg;XG!Y?^>LciZ`r_dX4mVHD$z~`~KgzyaCP_Lg!oZdx8roM%9HYfF+XWX5luuqfoT`xR1ujj#DC?{@-p{nP(
zR;}?|>8MYh8U$`}9^8Ev91R=J|@53GyzA%@kJ{-Vp6TEA#`qZQLaI@6M#UCB2Gq4i2WK`1VA
zx+hz26C#lm@WT6F3PZ_W#!>uhT;l_Yk8Awi$co`F;l#tu4X^t!oB??ieJU0f5mvUF$K_s0eX3@S7$)ZDw%RiTM1KoCFTcyg%
z%nbX{$upXT-!_hdDN*P}7n&yurf8EW;i4ONb*E*c&6BCCccib;b2U_)WGgf^C{-0PGuw#n9nggy@l2zci_
zS~jCE(P76UshFgakNy5t{{d3tk|3{0gzMnFinpyN(eKXCSfo3l)o@PDXKy*w`}FaX
zr%jk=eokFgdYinS3phKuJM~lKhw1ht8C)JK_L-y75yrkev=D}TUScDhynJ&s$TxpX
z)yqae$*S51i}AeU_UvOq`nT)3kedE@%Yd^q(zq^AZfN^W4h>&;{*_D6Pk=8T&I1S}
zo-fRw!tF|7R!5-A{`bx0F+ywiMfYK{lV?butEaHJqt9mK%U$5LyVs6l)hxK8F0TGu
zlJ@_$2v0~k85}RLzv>wnh}G1SsskoRF-qbu)dNW^N;!k627SVUW9DM@Y5ANZxP^!c
zAcyqE)OMt_@~TZ7W=4{VD!H^!xVgkEU*aV?*3FVO`KBiYX%
zwM5m!B#$4Sgg!k6;v+{_s#}f7J~ar!=8J7&trGYW%ga$%sVpwgv=nRKf@NK~Wn}Pm
zbFQTBU3xA?qZGcT=r?tH7ShAtb-D!JwPAjsR}@u)%g^+4?#1p5GgN*=o6`T$@rGz)
zAE3AYqUWM}s&Vt$;;?WmAUmRsS-Exsn_fa6(xcIdwXve>#9m}M!Bl6t1XreR4!*hmvSU_1>
zcc&$UvGuk)q7axL&-1}0{;ur)pj{KQ{->wZNKMm+&t=Xy7sG9>3taaWAOQLs7HV^S
z_oH;Gohw)43~qOFJ`fo&Do{peix(@|St#QcDY$XC@^K8E9I1jOas2#O@x#5KD%kP>
zu&8-09;E0VWQ@}i%bWk2Swf4yAJMsiN|23WbEp~O1e0*Qz4zeu?c4uywn&klUx-kR
zBgHUOW{Rwe^*)4GD?2759Yg<%c9J{?yQ?)obXB`59aR(1CoW@-Vc$4^_=&>*R0g3n
zH2i=sz`HF`)HnZRDAbjutB&e9lx8xng8|UuhZ`Y+wlMJgd~r%
zofn5jK)3aPwk=IHoKfA)fT`1w?sd#Jg_lrxKJZaAa}^syzV_Hn6>4Sm_yLb6H*NbI&^8f)h|PO{H|Orh
zhfDQj*5_IXwL#;m;hW7M9Gxn;{o`A%p;(pI-ZkRP5mhZJD-KO2(F+OZi%BA=GK$Lg
z|4F;Qr*r`ujpdtG#B6XgxkE+Hohz^dk7EYF{wXBWNDn&NPjv{jA`
zZG~(ORx9)~SJ>QsPKVv$~97b4oC>NA0im^w#QtyXwA
z*LcrdLK8NWT?;Y7t&pa|BXO3ChbI*(g-Y1^EmchdDupR@$bUF*ib!-SJzQOWwoA+k
z5Y!w_r;nlQ(8Izz7P--hB$8n?_8N8a`V6wMQ%D_Nt{rJWkmXj=r^3Kg7E6r9cx8{7
zPMwK&_%nSWmiOEX=c{Q@$B2-A3)UrFXoN+ctvQUjItx}
zQH2ki3pxT|NrMY6l0Z=&x^oz}LMe3Yz7Y7(KE8hVAwiu4Vq?+vn1~o9fD?R?H}Bpi
zh(K3*Zyiy+sM-SHQ9^jq4K6*kM>%$D&<9^&fBmi+@>ccu=kTDSJ4b9X2I(DG+_T0i
zvKW{cQ}}LJho7JaB6+aCc|nTk7}r=7T6ldR)scjDEFHcs4@4&5X(D3YKSD#DMxYc*
z>e|i2H+PyCPKuTOh5|Z9sU|#0y6@T%3b+QW$d{QfB;3c)#ZUO*l9PfepiHH
zeBZ!*cvOlcwgcuN^Yp8efS9AX6?}zc@2kA9&j0KCe|&VxKZL9A-R<7A;V&`VtY4r|
zb{PyG#RUD3;QN+Jbm18Koex||Lq`UkD(qOM{~{(nlDY_dpap4`^Uhc7@O*;N0pH=P
zWhXvW8yY(Fi&;o~G4!P_ojE+>5>dzknOyUQ1#2S>b=3f{>L51zmUCHm;3Z>8rBamXs*
z7aaSk7UsTNN5FP`6AYw~MT@gTM^8CXB9uZO^aX}KI#oXZoTGCn8XUMYY>xqBB^HSA
z_6JMjM>=&Lv6y+Rz&Q4J&p|1@kNFEmrN4#I3Ckn*OalPgm+jM}!te)J&%>z-pq(ZS
z?X<%dlWGi}gd{*fI+9hnVFRr~g+PFqF
z3m>JCAUXygWpS)OIP&(JBZ}Yk93&p(v)Yh&K+r_d39MmWf3{=dXAckV>(Aj2#BQhD
zMOvc@E!fqtKn6PRj;iu6kOkU3hYig_yA!_%ih9;tr^DmosS50GMoX+MG(dAd(8X8~
zAX)WiB{VF2Xk{YL9Szd&A+Cs~eB(Jl2bX_kRUpZ4)35LLxa>5YmthUXv)g%>8N2_*)
z_6Vwb208j@;a4!wF&fV!B=&Rt*A#iyqbuxhOVvh=`YWSOS7cXin?>{58~sZiQH8{p
z&u3o`-C}>JBYpaO+()pwKN}|4C^S?f22gdS6~F(>X*dU)J(@wnaST0<;0nDck7-A;
zoM27!p$iJxmoKG@+K;F^nD>CfvYB%jl|!tG1Ckv{mI_Z>VTwC6XTY&>
z1IJ`WN-rBRI))jYg4{zJpiP`)p{I_b2bzErx~2Q
zK76~;P#N%-Mwa!#QAlI|=X5*GT*=rI+MT>+lOqcwl_8STV%TbEMH1Z*v4lZXUC>(a
z@Mjb8MEqP00vApos{AkNA|LTDV%0raSSdq7qY_vt`NUWv1m|_RC&nLkJR#wVkRxyZ
z@$vGm(u8WZx3O^bpf;2UHAO0YoT#dowzZ1khA-IxBCQxu7W
z)^)9V=?5ZRaEde?70C}@R*^X9d~|PNkWc{SVMO+?BUws^AG6{pyh94bu^YcX3HDDI
z1vclXi_iZBnNW!#8}O1z3^rgJeIkR(!ptmf|IwP{5#d)V1-UEcyI>?5Y8(HX!`Itl
z*}4^hxJIBQAhwo&I}*?V{?aH*LJ!~L(ytslU(`=LzXP@!EJ8ZjD@WJuPk~S}RE-*W
zu<)m^L6UVM$$WA+R!_u5GJ=ib>Bnf`;D&k{3(~0NsR`4gGDr|?Lm8%OF$U&62?!Q`
z`}@g>AT}KQ-bzQsWF!o!iPdo{m!6~wiiH9C>1O)zg8}qj4j_BX}lURmb_5S_)xslp~
z)RZ^OTQ=v;`CH*zaUEJZRw~ZLSq9MENym|8~hQe3^G7c&0guV
ze=;&^8|hSdwglZdOQm4=D>yw2WbuD<1a?090G6(5q_>UGTbs2mHD?Ci5;Fl?&QZ(a
zL1J8+@?~gttIUIn4;#df|)3-y-2t4)iNY;swN@8oQy}p3qNawE8;!kLcVMhqw979_eW*M
zHu2I_qEzwuZqP)f@ARooh#0i3G~2abEw0+L9F1mR4rVOvQ8x^67KR=X+0f3=h9AH*
zAHb*@BRW^#ts|k;b_)*B8GmbtqjV8otQ(A&tJ_WiF@G*n+aCI2`KnwAbVGj$atIk*
zJyxd|*$>~o8jC&_-)nb_(jW1yOd2kI$T|-hmj9P9*m)`w3^w{zd+UrAVljPJQizrZ
zwxdgIyHvjmA8g6K6$85xo_Wo+Zc~l}B1lv~JKF
zZf}9x0~v7B3U)h%Jo6=d@13kpqSkx4g^E{c*Zw{tUBoWXE3ZZjtt}vGSxMm*)qfor
z(B?_90ZRd%yuk8J^W4@mm+@QknVc%Xu&1I$qB6&;krtF*_e)Ymx|SNGMY&uLK4@82
z5E)#%X2n51{1LGjcQe`o`VFVWf07DJZtgL6^*a?mL)do#2Zk;rb|NjW#WIEh#xcIU0V
z8F_O+{mr`g{Bz)?!EKRFzq+yWpW3Sbi
z;nV!{1xhSx?mJswR{G5Wvvwk4L-%OB!~>B*>|hGP((!MxV!w9>Pz;y@-UoXlNtAmg
zAZ~M~wQ2P@oDG+vPw=oS*^;0`9fqsXdSvrtUIo*a;F>Sy+T3z##b|ckJ*|3
z0dk0{B*Fd|<-xlteN{&bFCl1JaG+z@QMK`(V#h`fIS`oqhdHUKsTq~me>-z`P@6KQ
zrzl+R7RPg|fBq)VJMlaRU=)$+IaRxaqBRXP*p8uZ^-hh2x0=YA+C4xbnIW5DRKYAsPO&*?5h69
zhbPvAza4n{*wT)$H(Vs->b)19182<0XyD|w9ts-$3su7zKf8J=*9+Oe22{Tt=mDYy@@dBk>iw`BF~5q)b7W$hu!?A1?eF2eKd#_7wloqrxr3owTxQhEyXoF
zWyQslO136rYT&SUEyS0QkAP35n%e(jM-~gQ06=P_4gS~>8w>wk+=o~G6q@RHHY2h<
zXra}#FJA$a22XE)CCZ$eO&$p_ROxxigX~*_c!OwA1Qa|Gi8I8w`ZxQiveI|33}a2j
z+RJg4%|%vPRjhR{MOE#-(TbmcMNU`(oi;-20Z6Ub6J?8)?&`*Ijs*SMt`iZDCy-~z
z6wy{e5}Y4nR>|w>M`LC+(1j%alSZz9q8y#@p7<)l^+63qCI2nw0Hl)x3rCL2@@U#d
zKRJ253X@DE(~l<-?nb`o%Tbi{ve8dn)CQig=~lRJ{F=Hlhyiyz?Uw{$;ao&!E(uFanV$#-c{kR2`FEQVEiZ2w3JXMFw-aGieeVWV=R0(U@V
zxEPGi!kA5w%;}4wlO7JXUWl=nmcawc7ao|-U5l*%HJkxg%jdG;?A^L9H9hjtw9D{G
zYt;DOPGhRC5$H4X04Xb-d_+jac}j^I+QL#n0NNGioQ2X^Km8g%-%YJn)1GQy9Gp`R
zLZKU*`Pl9sT74g@cJM0c@|Rx<0CGlS!R&IM*F@PJg-n8sz=z)USb{j~ds_~{Or3Q}
zR@z7yT4W=O-Usf8N55k-UejY0>1u-MfF9-um}~6?#v0~&^To7#FC6G{gGU3QlhqBM
zp3&eWFp{XvdDjIJSz1ZS_|+GI(bw`TmlxqY_$v#YCXmXm4xNp%74;mj&(lY$XWHhi70dGsLNQbP;
z($-h2uQxIkPw`IC2gD0dxz%j#S2AJW!3l1YzmOuGiu1dS?nP0xeGgT1h1n8Dl9(c7
z{b->ZA}uKh^K^s({lUaC__v(3a845`Yd@t
zA~qq}yqUA-Gyn?uva|vWeG8X~2(|9(q8AP-*Ygwhm}(Oepw*bG^IA6Hue$&S)eS#(
zrYiMEKwexJLF_|-I4sqolx^BXPesIi5S}midSh38&24yR=)3{CeitL13Ai)9s|4W;
zw??xkJ^e15k&$SV6vD-Dc~-T8mhQ=GG2<+#Ue~n2jXg5tzIcXRO8fH~)aab2JR#m2
zLWt+bSZMV{8m%}Zi9}NI9=My-BxkCAcE~(_RX@Hx1qY$F)DEX?RdBj7p;d9&*?8bh
z{o8mb2@#mz3Ua+jIN?#&BfLhafXgYp>ONKYyHc=stI;
zMW{g5(k@t3US6n$oc7Bolx2ks6j5$|IMo-?@X*E2NRfak*Onf8%oY#ug|$BIcbMJT
z>m0w^N3#Ga2)|fn?^}ZqF@^W;a)D7}5aus{YFm4jnr_3uprm8`u6d8`eZ$?lepavL
zAm51imOP7y)S;U;}vu<<^WaZT#@vGc9ev51oxoKA|1p<3GYRmT?~Ys~lkgq*eH
zTob|Au^)e-e|@^$aN@0R_Si6UzE@rHVBL8=?d!pt92CxDkEbZC5MyMk?foAa8u{3i
z`Npj*7Gh-K%yn5M(z8kl+sYR>&6IPEW*W~0EDpEuFJ6RPoK?#|%4%x?!1=LbI&pWu
zyFz+Gt6PwJm+LZQ84NxsmfuJOHwdWOo}vR{*Zk(xZ&^bVDiB7hHz0Q(Og_G=jEZEb
z|B&IQm+-?+hYD?*NL!AbV+Bi=Y`nnxrGuUjLX{#KDJ{VZNz(l{93}$BUKmMXRez(4
zmr`rS&=;W3RkvfK!!$r=&$1#D^4M&w@z|IZ44h$e
zGJ6TlvM;Di)gz{_>0n?o#yL&e`sSE?O!FhZz*J~&7>
zdTwi83`RDU{HD_R*+9muE3xEi>Bs6Vufa^qu%;l3QmZQXzCEWYJ9IYtoI)(%P0$31uJ2PS>d>tJWtA9xZ=;q8p+;VN&Q
zjRE@_W8t0NrDvfY$!wJ>)&W{kNc}#;PW{7-5nrccKO@lF`boOaSYWeV7Gm%PNrN{Q
zEMdM|lKn&1=)C5>DLpZ9wKRChHtz-`{%cy_^Qkq#Pv!%bbi>dWyk3BG0pzp4!#cbZ
z86bVr+11&poc7r5?_&~-S?m2&2$0*gYkd}HR^e$h0w@bhK>$F*sy;Ut9s4+)FFb23
zR9GmrL`P#FZzBaV_|L@Qq3OD|=@9-N?6u6&ItTRl-lppi)3W0Ma<($PcHxg0o%4|F29Dbv-%RH4
zuu%4Ao(Fpy{$Ln!pXm1(6h#?e?Lfcqk0f?VkE8f3)zdqucoOnreZXvqGDzE^p2+7v
zrnc^hQ(3yV!}zK%cwWor*aB$@vtb~pdL}!MU&iD@Yi5v^6P?cT#Z4GxsCW%oU)Z)z
zOEZtpYKM&QUDg41EzwKU9%Psew^`KO+m^p4+k0;|bGT^dV(U`}6^ZYHi>~r!5)&|W
zvQ070xzHIU4w~tit8fUPer31<=aCV=Sm>%_UB&c&I5|E&{P6s$QZ-bh#oAY1ql5{V
z;OLXgkYNb^9ES{2rZUvd+gk%aKuV_}Ivd?wSXN1gGq|y(RPINRgVCPavZ)S`R&IvS
zO2-vu(Q7VC-*XQ3cbs1$#(UZMF`z1vnM!wnYZ`IS&&YxqYxwx0cb`^LEHs-zskTV&
zJ%@7)U4<}UO5=H-Cf$ChLtiAf3R%DtgdzPUdMTVh0e6~8tvIK3HrnYc=ps*5iLy#(
z(mG4+7#2VisWq~t?MzLHf2pn}g$%L9eDi=D^O5!8KIQEJD^9P=It}E`+S9%Hs|U&I
zb6=Xchdnrcv^VlmDKLrhSPzxI7M%PQt>Sto;c|!T$qzU%$?P;wh(8eFL=-YpZ)&r7
zVJi;O);ycvwQA@{M6&8pAatG%moFrhq4|Bd1T@kld{oQ4OBE{U@YW2oN$lz!=
zGQEtOc~T3!q%{cA@dB-ee%&2#pdaEg%*|3CL5N!Cym%ns%X8vt
zwx|*_dx#-Mk8{?&xqZ=3BdF%0b>2HyEZ38z-bUuDKs}4m9M*jHtI8I)l(oVZREkV(
z95?ZxfwtIFC6vnp&(w5~8k7nD5u=cts){glmbES0vQJNR>x$?qAQx8yJ78Sc29S8W
zJO%0cf;1(cF)R6M{H{c2ntLl8vA7a+P9fSX3wt;^kmKd&O%PVRY@E)YU0!+0HKdGx
zn6nk+(F({~?u4`mg2t>R@_Cb0^04h6JcTq{h+Ke;-_VYTatI;k=K{0KwbZnXJVe?+
z2Dfu9wF^Nr=ca|2@PN5O!gPt?3un8v?VAx}C%(&6ob!!c+I~l%$D7_0>37G$3~S;7d0+1%d7L>iC*hd&=w#Ur1gXkQWL%vpT4W8r
zw-I9RMg}11xvFN;(yPx`F8S7$7E~8xDoBKoMmPnn@DT}+FhR}(d7Du>jpvdlg=VnwJ0^{F^V=jOH
z3G7AGFbbpqub2!5ITT~Loko=H$(
z*~S}QLi%F9p-?UTCMPEs$fD_s&rZ+$`Rs#}6Dwzqq5sy5&N}x8Z>l$LU{*|XCBCIr
zpJe@a@!H0p2C+h4We|?BCgt&&*LN~tU>ccXTzEa?s3rX!QGTMiEH%yY&9f`G3bQnc
z)O20%_hMB?E4#rdBA_I1m9}@R5*eUdhS3O2#JU-<7?ln>Rbz93Ir#oeRm7YA$nCFL
zAAwou8nZp3=4EdoVpXuJVD`$2HCs`rv1{b69|+C{rh)G9K#fE<&Y
zM8|(h;R*g!FdOGzC^0nCQ564Ui+|!?p%F>^O$&`Ufw;r+8lZjs9}Yv5%83o<<%s<;H8JDDk~Ni{DX_p0$!7KA}$orT1>y$adu|bP%Y#6TK(l%8P}kSKR6f8fQ*)lv1dRNhMG=T
zXOIRFPf~<<=sdalOp-dcTa;^gg<-~kpllOV3VdCt6Jf1hu*@;hRN#`QC){Z^)6bn(
zP0!_=25mgmgw~HvfaPcwKY0Z*7aGZ|73tz`92WDKd@^ULr)u9(zc?wm
zZEVLG8fLUAg;$$?DFFSll}L}tSEN<@=2hg11BV-lv4UIw;lV~d!Az!A=IcGHngkSAsI7?D|L9Q(A>=azV(oxEqUj9v+98Al<
z<~x<`*l;gduHDVZf5^t*u(0iYuz2vs38C^nuf+V{b{DIT{{mu}s|(-?*+P2At=d7)
z(NMU5G&NMQqm&^ee*X)^v>^tQH-T(UQB^Km-Ns(Fp~k`KvFr17=^$q
zFvq1(GkHNAAvxvxO(u=gqrkvr$k(A`5w5A08QAKzo}a4I{DLDTN1C-zR7PC$A={8W
z2r}o&3g=2oZY(18$6yD!$0WDB&leiL$T(~YB{v*v+>K=rLz3h5Hi7(IE{AE3S({-2yn|t_7%O;$@Gk^tqsS@*h?bNy2Y122sOZxg1h3^X2&>_)SJW(S&~a;zDMb
zXghItfR5uwpq4jCc>Dz(!}^@`Py*UxRc7uxMB3)RUS&Ki4$st`L&Y%2cmoca8H8
zIJ0ulm3&=Q7WN4&_WplZ?Ely~k$H?3-l^D})fWDq1(b?c$4N2^d=T*Tl;5ijs$8;x
z>oSt*+6-2>TJEUn+0t6%Mg-cMZn{2xO`})wQ|x#bAK3H6|PBuOKr;
zw3>Z@zqP>HPtD}yb;D`|B^Dz5gVJ|R_xwc>$%AngUs7tpSXY$i!CKT1rymiG8@>BZa~p
zztu-zI`I+1V!#-C$jMS2i5GHKiLcTsta?wr$)e_gI<&2MU>DZf5R(L$v4Jrs!=w
zJ+mEYPiQ_lBKiXz29YhSG-#u@vkER}cUUjHei?aF+g?WTnxiZ;*1ItIN)V)!7DE@E
znxy+DH*t$%gQEI+0Kdl|*Az@{ZWI)e^AI$JGXpd;L{{>Pf;^7RzBB8y|reTe=`W7
ztT9?)R*yXqPMfDwmgHfrQw~;;oa^~BpDXExU@c^Ppx1TNC
zrM(0x8V7u68CfE`mR}V6>jgk;7x(^}S4z|I_dP};NjL+bcKE8?G)<2$W!x{>yE~v-
zK)c+bBepaUfq|rEAJ7)P-h>@^@j@>gzqmVi>Sp(Lpkbh)OxdVevEo!kLxjAod98t3
z@cbjn-Mg={UA)v~Iv5aRbxxickjYoHykTzc0{L@LV+_9m^i}GIy+Cdg%5UK_pLiXn
zCk#5PuccBrX?;XO!p^`{7^tLm_G1WOWP@6Se{j_rE>rOJCY0PUt|(DlEF0dl`et3V
z*Y}gK%2W#956Vfl&Q{}zcD8uap3cq!JKM7CF)Mz`y=jS>w=q_ln*sg3^)6%l=ZghG
zwCGay`o#-E`$OvOsl&6Nu^1r`zJ}+$DaHfq6K;jaj8%<@!?mSZOu@#VkxMglwgU3D
zpDca>XT8?)PO04#vNa9bnF*~Kb%Uf;pvrXK`VQAzU_(mPit*Qhn@)W#EBntGjR2;F
z@iPTIbXENB9*=@2lRkuUSGKesFl#jyaBaN!%XjfWLo(Sc^mYwC>kh;9a+5%e@=b)v
zJvZDp76vb>C92r%L37Xa$%e|xmq?BsMauTD0FxouY{r@F&U}Ad^1h2|+y%Q2MGdOJ
z?ihUBYU@PrBp9kVazhmjuc%O}X%|=&YN(U+!f;Kk@$Q0im5if#X3j<=7Gg>U`Se
z!N$(iRt*1
zy?M>|XU4RO-koOdPkP(k5LkjegN)m&={TyWv`z#wp3y2g&Y020%=bWr0p!fpwlD{s
zHwBx;bu9rwP&^sRA}{MN%Qa?%r0?+A5<~(Ou@bmS5JV
zNQ`?kECPsP-f=30?#^AHGI+llB4BI0(X?Ll*ph+yONYBcGXS7EogcAgFy@3(g$kjH
zm4HBn8&ppU4N+%FkeCtptW4*{gYhS%OgN*xM#Z$%t$zo9}4ez!(H*w
zvEp$&`7*3V)iT1S}<|XVa)qxKK%P#FZo^U!AI`P!-&bgc8ZInTQ
zc7b+{nHTLSIWF>IV!+Mr&qPWHZmc9&m({eiTRXSP%F8=fCAh_Vz1_(KfVkb+A1>=%
zmImrt-KjRkJxpyNVJ|{nK)(>z;V-h2>VMX!_bYo~(7}WbW7JC7flOl?fD?LFRNFprj>lDvA~=
zUJ%FM#?jlnL9O1O;ATkD16^MJ!o`?xZxS@dmf%EZS)jcao;QuOS5w#bbpWcIvR*-f
zBU4r%CFO@lsoS-{+2N_sykShUR+e@=>l$c%G*lwdF|WGspkr(^D$K-WlRGw0$`Sm-
z5>NyI-|u2zjXheGRxO#f<~L}~M^ZwnXK&F@KzF58UA$F|=(+N$se=0kZ@cfNU^3Ok
zM`&C%I;r|{G(J?r8;nry+vD`YFCsy4ke0ta-}LY;LMeP-n0o{3=wL|CP+WdLh*QM+
z_yvM%9e|IggnEqA+&5mY7YdkA^uVH8KN(zgwQ7GYTTS3N+v(~}FiuGy>
zYOKf5o0)UeGaCB_bUOLbL+FufFW-VdQ*y7Se`D{{z;xO8<7+##kZnT`JfftL`zthI
zN>_&H+27?_BykvhdCCJr22u9kA`)|Rf^gNa6?fSaCT@%-##xhS}!OFHuGb-FJa^&uYflS&1KdaSzytxq$=hD>&j5-tnVb0emwG)TlVi7V^PU&zO9j
zD9KZ=^PFfaTxd!}u`JM6G9D&FY;-}wy9YkLpoyg{8APnodk;#oEp3Lr<6Lj_RG8l|
zPWtJ-w2044)mDzaZ0)(T->Sps0`Ob;e#9e|i0<#cX!>WK>O2&&K7oPh&Zt_pvfNy6
zw?kE%N3q;2`1qd;o{0h8E}d@;b8sJsRPmsbmfLdOd3VPjFtE;5=Vi-IW|rW&e$}I7
zI34>O-QNwIr^iu7*!Z|jA6{SrA;qRh%D4STl~PJ`qmduFf9BviJOrrF?F8CUk+MH$Hm$KDE+kWrGE
z`Fy@^H|}%kdH%VtZk_Wxzwi2dKjZy=f1+j}?rj48=N3N{A=HGOc>evq#(BYa943lB
zfc!z1#`pU%!!o>^G*0cLT_tJ)>^o9m-=&KHO#d}Qyir6ThFa!VNDV)}S&5DC%C7=8
zsAy2lRjYr3ZTOcVWj*PzTwE6Ngab(gJ<_#23l8mH^?`rYq|!)8ZcmB|bwZIyo3gYK
z*Vl+}hLCkDxK{Rg#R&e&$^MGyPtj1b?V-KS0Dvw9GJ_(*^1d7&s0j^3$iBzMMl0Z|
ze}D1y4)`LELmPZipNy;HUcJz>Z6{vs`W=q_o@$ggh-IK=vz*=AdJ3kRSH4pGc1lW0
zOJVZt>i1vu1-O<;F-axyJ3vEYKNy?1hE}yTM=cWJzCnN7CxL~AO`w=0fJe}L^T!R_
zZw>vv>;Clvk##UbF0r<-;tq7`7AZN62uFY24OB!1A%oAaA+??W9pl!G8%tix-;jj_
zqoy86C73}jgXi5nNVKF`GLil3SAK_T?${LAyB56P93i`M$K6;e)PT``ZQ@=YEj1gl
zFMU{KBgq0?kbwT_4zAA5&MJb!%ENx|_~kzU;?^Ayx2aHsQNjQr#?s2l%Cm5Y6gJHo
zFC!8N(`4%t*A4rK9S^x+LO~-h3K}r}5^^r0GG$S5+<;|Suu=9%Yum4VoQttAYO33b
zq5vK9!QybZjzX9v0`IebZ=RJYiw}TzNQu@4PIfFUwvKm2J+!^yNb@KqFs
z8&eEfhcGFhT`HN@s4R=_2pr+j>ZDWE0Vf(O=1QJBeams^XWE#`{%L1)>b`CW)Tk@Su3t$Q~lR&-v(xpqGo!Egjw;eXp
z23h0F$=Q&^6{SlrH4UTkHd%Ax-LVSrCQ&?8U;`ts;Xz)}pLZTeH*t5iFc5g-)|*sD
zQ}`C1UKKdCviyj~13w0nJ-!9LsmbwjGy0}pa6>Gs0rQ$W7Tbij
zca8iI2DUe+fPTsC%s^`k3+_u&t7he2lPgdUTYM95GT^|jq4SWQlXIAXUUZ$sn&gGG
z>gj!E56gTXwur}8f0MN@0_+YpyPdG1A}LA0psMZyJ-o^gR3{RFjNc!w4?I0=WK;dg
zooo-cEQ@>b=C(&!>W+eU`iF(y#L0#|_Ar0wAeK#yguqnOa&?$z%@OZX1^cLf@8TOk
zQ6F74F0$rUR=j_hpND9E5|XaK{2cgYUbKS0to?LMGrD&cd>+u#uz|(}U`cu3{Q%7=
z|MgEAqcqwS%_<Ax1qwUY`gUUVH<6wAc311(n#QR9DhvU1
zk9PrzQEbcKmqN+_pXu+U!@oDINFI#g$@cVb5GY?-o+1WXHXdB`zd!ZcR&0%Q-|%t6
zr^JvA_<0ItA&>Nw6kaz0N60p>wE!S@f>l3y=C8f5fjoX&)J*O=@j1&s8`NSfsAf?4
zCp&@-iz(iL<9Sq7vkws(9U(Na9emdGuWkL?WMW!Z^{PCv3VDdEVwU%q+Ar#U|^0&UCv%dMJePWY^TAy%J(qTjF#}t*DM>`*4ArW
zeU)q{;pOEut_uMsW`_{v@0tAfAMkiEUVAVq_K@SWz$-kHiUH3xH~KXs$s2);qPD)Z
z5TGV`vpGu9en)&vF$i}=AVe@r;5_lNxY7~nrr!PJ80GVNQ1ZVB(YS48rvYmT#3wI}
zn1a`w5jet~FoiAjD!Q^{pqBd3^}7dabmfyn(=Wb3p?e>;{Y)8NlcH4U85mf|8`WtZ
zuC!m|nD5zUi+x{{P-~|9Bm0ba(`%LZXsp4N;OR333O(<3zf0nJO2d
zUc{^QBHi-%x(?f6xM=~_lNv&Su!MsE%S-N<)VdErh?MW1;Br4!-5=SIxjK-A!R@GA
z(=u9(%b0QLD5$Kr
z@ULLb9oP>Ho&E3_$9Epd5{6|<31j>jfM0I9S>NH@fpy+FAMKb)<#!t>5G-O4fc3j0
zJ5_LcdZew8Z_O^Q9tWs}lVICtvIOcvEZziQ-PYb0n@ERbVWzvK&N{6ITM%s8D|{_N1SKyZ~zh;{@D1jy9JbYxwg
zj-P&bhUz?AHD7Js14)GgAs{kq{tw$?4z^SkrzoaE_X04|ji{>5Vvp>^AfO>fmLJ5G
zUd5FZjey=p;|94S5a0g6hf7309Pjx_Wm!E)3=bZX_<7!MmH;iL?4b4^qi&$QuU;C$
zFkuK@I;FK6RoC`&O!biQWqD;{ISavQ;KgGfZ=)o}^p|ds?F|ROPS^5OYwH1I(m2$V
zwIuWGI@S{#fC5F*gpeW3JkZ-NBCxVwmH5P8+hb7uU(ELeHj)OJ_YhE~VNdW`^8lDq
z!Rz!hR>XcOL-2Y&$ltTLXh1%wso@;!@~iBU1bgNqq}aKpyM%YfY^^?bbTYG>%;0>3
zx~2$$@oTs*lm@dwF88{GR$v+wx2x9^#w8b>_w;=
z+NoNe>uxtDK0v;k+j|tQz6P7Oa%)c^n*bXey-g*Fjop+_VetkbLp5sC2}Pwxh_}pA
zF+7Qow2jbyOe>(DH;X8wJrpc_`C?$3j)*Uobip9F;U25&v8)D~lg_}_YJ~1Ep&C-)
zcdj`@@ps)1NG1Ih@9Rnd4`&_(G5c={74YA1MEaRJ_6@MJwBVYs`-U7@Gct)hjzSn)
zt=+L|8J7K^_SgEX03fC6q994zq=@5fVXp)IAP#C_Omz&LjBAqX<||ycV5p};<;fOk
zP@zvLBSsh%Wa0obF%@S%umdC`WJpkU!M?@BXH^Pjm6w9=hZxGT*2e0-jy5pu1|dQ6
zwV`#}k#;s_K;tiOnMY(?8U}Wt(d+YG(rTT1elsLaEFa>{b4@Nrh>L}R1f^Jh4pg_}
zko+X4s|;z#c|9n+;voHHS;+_aH|_-}&Hf?B@FfjQmm67FkHvCh1i&TjMM=JjJl%yM
zwI&Ekd(f!}Ii-gP$M3dPdlL>Qt{T?rrJDsexm^w$H?0FaZa!&{-yO911f@3ul2>M_
zU}mf5A(`51NqE$}wg8<-ZgG0~mBQAeA;pLy!B;r*iKRxzJRznLL7DGvH5&<`
zn|L7S9CdEV(TRo0Ls}
zgTzNn+d}@WR(2W*sunasg}u3V0s+UN%y4CSX*)>EeVH%i;-J$74$7Q7moBcJxYbs@
zUTvc=W{zv%l@7&wz|3;8bTt9%AqXApZ}$og*83wB=?lH(J~*JW*N*f6#^;IVrAta9
zm7J@iNeE%L(DO*606FBah3dyGg=4mr2Ldku`P%$r2C}_$s_J_o`Z36{Bv}0Z=2my)
zZbv3&CSiF3n6k*dVj%pCMx!drs_e)c#@oAYc!TeSd$CO
z*yihkA;XYgqivR5*(F#^DFdxFkWlR)#~TY}265R4@q#b8Y+*U{y3S+S2-^p24lx(N
z7)e9z;mj^^=bHoj7F@+I*FLp_%alH401#M~AH4!4g_)Br@H3ZYz-8d1RNRkim8P7#*uP>UYLFB`HWQOgbAR`M9-
z2zUyNPE7Fd+~=YrJXZK5T)ZTk|;2)=J*{`
z!XNP0NC3L6BMO|3Dmo_#iNbnDC+#xtIN;HXTfsRp0~0?y>8ge(hX7oC)}7Y2lf+@i
z%uIzuxcGElT};s60UAbAJfPjyoTB+O8^L`B)bdq-C}t2%M?UWTZ7@&{
zCXEC{QLJX$+!op%$8Bd7Klv44d1fHVfdl@p%JKCG2Jwl)J-
z?!d4(AXK)zE<+u<_%$vb#GCAV0Y)<<1vlG-!=u14ozyfY4teHg(4K7&TL&Qz%HleZ=b7&+74*{Nw=)5@~y`0kR{gAndcK^?%KgF1$0E-gTx0qFqe2S*C$
zPr%+PF$8qZ`JS2bqo_U?ff($CgB%zPmm)21R!mqXXeKd6d@nX6;73*532RkcEAshfwDl6kp_yGF&lB-`P<=C
zfN%^-R;&FdS#p3BF@QusGmN+cY@KsHaDBNcspyl8O7GL$VfzTRYsKUbPXhMIGo32!
zj-JW1CWdoP-Pd~tJ05=$!voRAAVPA?f|5=cA`zjoX5d2D0vtMelRI_bLTo=f3(cn_
zz?qt(_+ke`nNZbx|H8&tF8IGW1~%LWC^Xhq$&fSK$V{7xL4D>Zg((d;Pe0t-!8(k1=!4(iY#(*ZRDgcmJ)WDo7S-g~l{~eEGS-=g>pBpVIGHp9_@o5krh{;4bxQH=kQKUfw3FH*h
zr|g?*^<4PdSbvGY61h%}6NQ@)^8PVhndKX3`x5k=!o>6D;T#b!Sz~AF<
zxo`MuH+o?YHdKp((uqgCP~fU?9t1*VQlv70-mz7E8e!{1fISp732gwx3P3$Va(oZ^
zFur>Wa=jCpM<)=8VgZCMhF!{P!HOOQ-#m}WoEef!;iN=RKWnH5^|OC0YRv&8+(D$W
zf`%Qz4huE+V@9C?Lugb>sU-opD4C|=9_X|6PI!o%PnIc0K_3Fg{z}->mL}m_;W=B+
zESsntD5@nF;Y^8>zxRjrR4MyN;BB|{PW%!
z_cheV-gchLQVwN+oD?c%@=1C!N#Z#!jdbf+W|TET$>n8p!#f6>nip-UKw%WI>dY1a
zohmqd{4t=s7#*Oh$*JM7IpZHKfD|~SX22Na{FWm&{azR~VJRp$TU1p9P)N|Wx*~%#
ze?HP!c~5HurGa9W86sOiTG|O|pjQ=E%(Xl`c;a~ZB)VIW@$sA*0p}98T($?-%CmpE
zPmiTXH)q6Fx4iT?=`5H0yqly;N{s_rRQX560KJumWtd~736t}c*wefHp83Mww!ABK
ztK4K$9Xwbbi7Z3$b-}`nnX}p;&uDl``fd!W>Ly#FTfQe
zhO}N)j0YvCC;Kf24X}dmVw`!3Za`&*!Q>bsh)57uqaHeya|b{cLb5h$kc
z{cLfjv6=-ONeF^|oKDYF??R5@$L~y+pDJM9-dj>Q$J{b7eLBNJWITi4?Pv8M7xi>EqTNWH<%OX|h_mIl$ay$fn-Iebl{j{DrF
z!$zGCfyB%8L8Q{vprKvH_XDyo(Wrr@q&(vHaH&QMaGL`m^a9O89{N2z*A?d%Zh*8&
z9Dwyjx&We7=6Q^;ms(f}7}0g~i{-v-%GMqp%q`_xU$wTjPFOEZS#uUP@7~z$coD(q
zMS<+u2pE>9$np|D$g8p*lnd9wNLO4aL8R3?9X=YybJynKPRF5!+nOLc(+C1VPnq_N
z=-eP)^Tqx|p(5N8;omxR7-&$7BWM^f?l%95X*HI)_ib};9DMj8(3~-h
zCx;H9#e-r!;;w2<%FL&7L`JVZWy6lD
zT25cL51UxO*y^#H$dxO`dWh{#vyB+~`4f@^s+g^)lEg_+Pf-nlW(0db?8q8U)UeL!
zoJJZt)K?LoMJO;JWkNH9b`bE!lh9M@1YH=jrkPk+b(!Vccu+j4O*%Hy{d4{%OO_?L
z|8o;bx(kU;gf>Z1=w=~DdwjO;3@~OaB3#@0#ufr9#4mRr6S}q+Fb7`xt>*!Op%D3g
zYl*3?vy;N=oq)9_HHavJpl#o{|0rPI2Z6DWTwQ$5K%Ni%j2#~UM-TRDNzh|Iev8ez*)U%U>IBgXtiAg32VTv1@Ia#3er#dqkww?
zaXk=LS;b0qgn9O_?Jn`@Zu)LqzQsDE9}qESQCaEOQIpp43-8r6)wy`7=bn`phLkCe
zyy*xXd)qt5?dQUGJ?$7lVxtpvzcn#5v0{uh5ZH3h5@w@?f6~MoZARg_D|W*F_stC=
zk`$$x;Ycg1VHTADP#=C$wIEJo-5*=?rk1^O$)jYe~yfiE_nvJs$PnAE(
zr#xHFM=;_IC^@XBVbDExe?&!Jst_*OF_+~#b
z6utNY9VaxK3)8wIhsD4%5B_>3;=FRM%UAr}r~&?*W<^QAQJ|lCMRf}dKSiK5$*=gw
zl3#^wJ;U3^Zp@)5Q1a{RA{OJ$%i}!ASmu8Lo~eciP|Iso_z^^kVFBriG_$CW*yx-q
z7|U|0Y~&jVaHQ#N`}w}ME>1R7b*R~MTov$QQEkVd;hEUSRmFO821mF+<}ws7b~-T0p47`!0!0>inlZN_k$dP`pr_yaP`F0
z1&_rN76TNmniD$ie0^QV_gs>g>B;1dIFo_B%=}S38Cn*^e9vG4N_n2h%%8X1e$&o@
zng7#`*C+r&iF0;-s%$qV5tP4>Ljp;j1#dbxu}fB?OY2U)r%C^Wo_84_^}VLVbAQ~|
zFh$wPhWT&g72ZrwklSb1tZ*&{wfVMXD!<8}L+(KSwctDC9^>jYabl^Q=}e>p<_q
zwJOdy+S%pZDA1JE>nu^drPQ(^S$U|Zrgq@-viZHiVp}&3;Deyiu{I}w#%Z8zr%Z;1
zT>@>jHzf%1JpKn0`K72BK+{iPe^0!On(7c&`C0VtIruEDqx^KN&K1`@7i6JIDJKd6
z!0aX2S)d8125k5ULEf9kuis;GG1QB;FAUCCx4-0`;;yEQw_|Nb*`e=8Wh#0&QBDe=
zi}vOb#%ec9e|U^ZR!GSuC1=X)#qMx>5`fE4Hhy1pw98J()7>GD_Ef;qjNcN6TJl3y
z9JW0CS_Y3{2JPG)nWaOh)q6B#*%p_m>>TNJOMH6z0ydMM2-9rO624VNbCP>X=fQ?9
zr0g*PB&g(Zv%^j-8;MgIk&
zN9;#Ft7(MQC3W-Xb7Q`31rBnh#|5dz%KT@dPVhj}B1Ti_vMeYsp6`pus?>djQCJ$=FDFYwAoRRG5
zCmGROpvAKQL4OJdb^&-TvF(F*I-WwuXOo6kGE`R}j7xBlE0OW!YT~2D)tJOALMi1Z
z_$ob|8q@cCeDF-aQo~-wO&+(TaFL^4mh!Hq^dd&eI+^;8Pn=S|3phiH2+Jg&^Z{}hr@jzmS4MXgvN`qUAqUh{I8LJ=A
z<7^g!f;G6xc-m%2PG{J?-Fq(@wVhE{EvxvnkPpN{H7)TvG8`EO2;16^5BiO!ot(=1
zEXyVwNHX;gFmiIAy!oa<-y`CXm$+m7NwwE6%vW?_Rn*QFL6Y&l2PnUTCEi6|eEA%x
zCENuF$y8MY3}WA*1TTV5)05mmQLM^Piphbo8!D}q2{NfWH0bFr4;;!yP!0p%8AY-u>Fpx0F(Z3G(|twxkY
zD&pJatRdXsKH2R&USkKEOtFxj$o+n53E{WR4hQ!ks&I+;os9X9h)$45CMd+;?&aOy
zzju*p*n;b@$5o^gVUc4NY)q&;dDOz8lDISEB>OD`lqLNrNUiOoXH`IoL0%G(PxZux
zBIvtCyNw!ATsUvxG+xIgZ_p=l_=>_@)pKCgb$&o7TgO9-V>_3gqgB;3m-J?zpUbx|
zFC$22C8ood`#MbEA|Jb80I
zP%W3+O_U#uIcKPeABTZ)%J=BvZp__WWf0fiEO-uvrgZ)OJlZ?o?9!VpduI&JIv+O@
zP%0=WGS`1pA>LNfayao26v4%Chrwn>%Awq)x@EARxHj?p-v{n*cba2wI^+`=2owe2
z(oy1emj}|hfjVJ~dWc;aof?mg$yhSv6kbxhCkJX=UFh1*JG^NshM>u1dsoFKIQ3kh
zy1|1~@>huuijlM=_Y`WKI|y`JX><#rSV%TiyHfJgf)Y>JqQ_Xg3D@Cw{3bN?xQuzT
z)*WR5&aDc`wFYpuXp;EP$B@ZDA;^%m!qLqwpmpTZ6$svra$auna!=D~u9h|>2S^%S
z{AY<8NLm$$+5J+bjek@O+7?d}GX`uAI9*U{j9HqmTOvL!Q>^@{5X=AisewIe8#*}C
z>&H)MNisx!OIp=sx<&B42r|4L$00$Rti(JTgC%`)q|2_Uq!%@D)wiC2+}{2!sP)|G
zlU@RwN9P-mG=;J*C^S_fWhOr{dD)>s(Tdf3dk-$>oQ@hkh-@07E7~c)r_gU!KXwEu
z0nZ=-ZaZGBLum$iZX&UH<)gbz+5IYb(1*W0sHWQ)1ntQjp#vx-&OnOoJ6!BsCqLoH
zGDfV}V^A3?D~~1!vclCu<4IwUS_q08OQ2TxS7HrE&eCNS{^sIMla7S%Z(GgkaMwIo*tD+X^<{<(-1MLyO;eqd|PN{!&*U1^)y|n^gXu>o1
zGypZxNKTHb2whlENp6DI5+twq0l=1^w6WGph;$M8_&_KWQR6(~G*%sHT2h0esu}nE
zW}X7%v1MZ>E`5M}q%8P%uAXA&<1Pd@rp?~nwwtgFKS7DH^mFOp?)YJ+Ha7RWB6cY`?%3@`WZh|ilv7pwOpf&AbzAp<5TP)J+Dr30=@ju0(!>L#?Qcr{94ajLv
z;NO@X#QXv^hsBs!L(16?r?eS}l6_fkmfqYbYQ(`xSM=yB{J6m_Jvu
zwM3MOr)CANEgwgc!SDe&c7|@_sFg4B)TGc^2iIU;%Ev%`PbTaUI?H6;ch%M7~0|uRzTn
zTNvny03sOc`9n{yo}PYl*o}Gap-Bvs#dx`sGvLHkI|2-#}zdE{SYsv5=tEU#aGktcFJ
zC|H8(b707XbwUh5X6$G!Nb0@sbp!z>ImmYEcowwBbW+@Qa8n{B&obedeEvS3u8I5uPa%{_F$uoLl(AkwHm9kofft#j?feKW24K%cU
z`@SyFh1D5Gyxf}4K-x)mR~~I9X!!vsBNuNE)fJBingxPgglbe`Y}YLGe^8ikqW;H5ieG`%!A_#(n@s)Gtb3XtP^Y#@wxyv;+S2FTvKax8<9;pp8K|pQ<
zqzSG3bBGUTf{j1-apOs-MQ|JOY0m=(Ol7iaatCdSd0e7$$8sPNavf4H*&q>Z)PKY1
zGraLatzpE8@^^A1%uoSh-r)+R8NG*OnTx$Wc^XrDN0L7tUxhXaBQt2#X(hgboxp~&
zykTD3zl<*zfEHBGa#kq@c;I+4&YFlJbc~5k3=Ov(%8
z?2X5d--JX^9ONHGvYw-+utMXaLXb6q0BU&Oyex7Yd6Z58gT|I;;5_
zoVrVP2{rNjQ>{EX&BoTLNMga5KCN8bH#p$35k&1&OvgEqT3oP$OC?hD*)n&84{&Up
zQ^KfUZ4+$xP$ZJ;uhlsz9P#r)10;NjD7Rv&W4&Dms^$e^2N?RGweC|yq9qolW|Z&Z
zv~U)U_?5nPAc`HTN7v=vkI)eYG7XWBH~htP+vB7(-%8KK$Xc?+UWrBatUDQ`y-3b6
zSU1HR)cEm#S#W3d+diI!Bvu{C~CvtjR!8W09kf{#Nx`pIiP&J(4<%KlAg2mb4gmIw&l*8Z4aTDCcXarCR
z$bXKKPQ;E-q=DenMKCxt4^4@=t{-cwFzEPz`9KI^-cvLh2q?_Viw@3ihczRrsiJ_J
z%OO9nn;{z`cpSf;x70tL+uKmI96!Ux{oD?TSN+fYJ`NbZ)}
zFa{KM2i5CEe&^~xp{%E79XQXSkclgba-f>h)q|Q?ZSrSua)`vF{k=%4HS(K-nz=(A
zgU-#r9`pJ0?iBuly3i7@6;M!@FXGD-t*38!2R{NZCc!|i6aWftvH*+XAao@e3KoJb
z;dHD;U7}lc-4>!8Yy=3`yI`w$_QU^94uUuaZ;0C9>lv3FS&w6ucA{OY#Pf9)`U)=e
zyuMP0x{%v}7j^xF=Dab~c6?d~KCusQ;1>~Aut$Cyj4s|2%R{bog|%cRS3hT(DPE>m
z<@5^91lxu250PZ_IXVCRjS}(4uvL1L7P(<={W2SyhrqQ5dL)<>dM{G&6ihS*)e3MpJv?+FlUcjAUHa4VEn{o6W(dm9KxPYwP74-
zv}I{!&p{AVMNHdoSmn>jdFz?RAkwl>ZJnz?>sz$+YWxYtKuRY;<4Rcd4L26y-5}eU
zc3tA>a2rz5pDiXAm%k=uJs&QHO6CS$ysD2>{vNaEX2rcEE^vK~Yzk3=l@*W&z&SIA
z#^kC3v0pLtN>Q5Ga6GAE?5_o+zzcU=AgVVc8_==3K{li56gjhh;g5i{==hiFo?da#uLAMG#Tr
z3sI|ljqI5KF75r+*WHXf&);8@8)pmQjCT)ePX4!cmi;
zn0Eejbx(dlH@L_-2`zo-O}?2WK?HRY;;F#-!x1!
ztuO``gP|&Cc^nXXvao1ZN?m|&%r#O+ZaC+c1pA=ED&UTAv-gwCSNy8uH1`+kQo#+*
z1}7ao$%X=2&(<3t)jnAqe3orK{h!Mi`
z^6CgcfUcY9OnYNwjLD?f-I?(HGJzgGV$_BdHOYORFLhtC=$>OWAd8n_K{AQ@5|v*@
zp?Xa)s4IgC887qI;*Fdn8lYxrfbye_0DD%*O%m)CCWL>Lt?HdiYM6Z=I;Kp3GFS3&
z{cc`kG;zfweFR3I>b5tmmp2iiy%C(MUk=_ZC+dY(a&H*SgG>KNq%W>?YvRp$s7hZz
z(1pEvJF3%wA)T0RgleUi;(Pl7buW(nH4PZNM#v)uAQZHMWMxDKsR3zhG@ju;+XZiZ
zRknj9hchCyc&2A_(p*x`)enMoy%?zL>fk9h=*fFt6aq}oTV|UYn)spf$sj1{STrQ|
z7QP3he&d5f;p&*gv+|qW?JQAm+*`TFHihwBIM&2XOl?4>z|JYHlcd%eiVj6F~oeE~_D
ztIEFFGY5hj4?ihX(>hTt0!(A5=>X&}&7;Mq&E&(*Mv{PAP5BTp$+0P}n))$=3FFQB&q{VnCL@d0A+ljWGu#MZuU
zc+nAZRD%%AeRX~DUDV`fS)tq5m!}4sKLet%81!qC
zP8yokgDlcfgw^1j5_g7j>Akwnk-`z`Q&I_W*pb7G_>Vype^;^DC-c&Jhfb38
zK@Q;W)G&4%*{9I(u?k#54DOR(-Zi&l&FPhVt0Z=@%v(y66KCqh2ytG;*c3&gj_B-{
zXH8=jth!0K{Msc-XUOexk(yS0t!BU{f`S??OW*fajmu%sv|xdATR(#;yR;oh^u{7o
zGEy84mi1M+F`N0L7KGxOKg%zfmxYt`I<;%8MEusQB>+
zq5=g0!kBm8!RmrEgkU?(HnC9UG-iYVI!N(CDM2GhqeU{3i(utbcO)tWlr<=&B6Axj
zdO!f}0ba60TB1w)}S$6-WwpybZVRNits+OTe20j{A|F
z?qYj_>Rj`xR>W9Cin9;D13y9*X@O~in@0X>EJICeu*&&U-h(CgEF-EjO1c#N$w#8m>IM
z)T&Zq{Z-#c7dL=NTt)tn40yA^qi6xfX>x8n(j2Il!^;4BR8uh~BJjHCDwkZAt6uOK
zB+^hTCbRbRA$~5pjnQx)NOS@1!R(3b5i`Zg@-M9*8$gLGD|fXnn{rHwW}tchpnww1
zWl3_rOl^W>uzH9PVZeLg&FpS>Pj_{mcMc7b4E8pj7PL4N>d4gUj5ybB2Yy|NxL=rG
zfTIr3aeL?6y#%_ek$k16hWvsof<)_IHz6KLRsDYWTUe0&uIiMj17(3s26AO^2|VYI
zJKA@gJ30NdKJm?A0#OWMJ^%`p)Y*pay_h-`X43$g8QqN=Pd|rtl1L+L*}b(tVj8lt
zjFG2E;mS)X={7|^r#@4ax~~vn(}=|`=(#NlzR;FA-?D1~8PnQs9!FfPyvFk>OSfhj
z`W}y^zToB3!xgrX5g&{x1jk+bj)q^bO5OW_8Y@Vo
z*#SExR$$P|lg=B7V$n`5dFdJEC=o54ARpHVzZ__A(Dnc)i9d>`^`$OPaPxDzT9-Rn
zBkl!sCq6G|dvxlFcW^vSvG2kG!|GthaS+3{z?wX~u0Alf(1LOK#>gr`p{%5rF*RAv
zpptdk9yq;j_!)Y2%Y(XHWvo{-99}d#lR#zL&9kNl7lSZ4wuV!R1C{>>u%c@adjmgX
z${}7_1V*qCG%}y=yV*W=zYVps)r9207FMl_o~W_=+cTbbnkP-X-tH=oO+R58R;Yv8
zy?wcX^cMlic%%R@ad+I$kP;0>Ut(CEXJ<#FJ4)N6sm1I@zKF$8a{?!cB~TYY(A{Ce
z2GIyIXbn&9i_C>OF70A>YI*WYzHRXzB%SZ>Oxh^<@DK|PRtw6-K!2al*9oq&t${Wga{
zstS`&;M-{h8g}WHUK2zqtB51pMs)()R-A|5UIhF>UMDh`DpcJ{J_;Kd1fLe9t3aQA
zyR0$r?pFIt?CQeV3M^(LNdX;r)hR(gP$YrJ9@uzV7-Jzo&9~A7b0wD8fIo=%MJ1(TP76kAfs&?T|Vm=r+`kR8E&lv!U)As<4U>kMNd@FaUS
z0YIlt;I}(RTH;u4e!%~MKffxX7u^A=6y|MrW7m2!Y~%yD5<^V|?C)&A_wPWI#dm)}
zsrdi<=wIj}<{Whti2Qfth?NIvcYXitM;q{3l-?knV_5{Tx>m0DD!=kyUyaOy6}!9*
zp(N$ckpPrN1|^y*>CtI6gM)FP`t7P(;Hvci<=2%whA3jthi$vxzxG>Epi%2l+e63b
zemba}11i1q)dokYe?9K^-c>;WT+syR!NVpmwhRgnprBr#l_d&f%^v(7xpKf(^mg>fgI-itoK
z$btH_M}y7EHkw4qNCdn21HV%g#(!e}JWM1uj1t6KE|N?=mf`#WcK`s7affQ>AH-+s
z`Pi^uj=(%%H(``BiSZlsH*JkRxDr>dnjuIVlVXPTs725K`$(Z>t$eDq`5(18;Mpv{
zp1p%j#3Oyq`v$^JX4M$>-E&8R4~GYXH5LI5q^ZIe02`5Pai;CBB{s
zI?wL0dU1ar9MVNKU`nT;QEdJPiRdvQwf57odg(Bu1B49WC*{1#k|ByI6GYsvbw(UnIH5)+r0ajyx(^X&B
zEP*WT#*%s8g7BSe_3n^vu_`{*WVGgsH3x(J`ek_{{o&7?%*%-I|DG)nI^LjvK&A@L
zUF1blO-;?J+-P95t}DvJR)p4<*+~r6cOw}RDadU>k|OIKnzx8)>RO=#w$v26EIEPt
z`5IO0f~zpmtR%O-y3-Fxqy+V3wAsSd24ym1DS(jf&b|RwV9j;j1W2x9^(8}qxxogY
z?#=SKQlPW89&a@dZdu~j`r%{11?Ku&vq_3s`|FBo+p4)LE(Y^R^(uoK%~B!>fiZuO^|Zo5U~@e437P`y7OAZV0@Y@p-#$9Ps{@rX#PV2I(+9f<$8`K?KS;u0
zpJ0HRL2v8?H1qYi`)oI0*XHfM#tbc4G=SaH&cmL-y4L(a8&r5Bs{(Bw*kdYCqGv_Y
zUy0gsl52jNt$q(YxJJ;|@svB3ij2rc{1`cmE}oSc|7W5S*#O8vc2))cA3&g}*f*7_
z6n9bfkG$Zwe(3O&Yr<7$DujoEG#_G+9B`V1u=0=}Z3&;}-Oz;22&p8P
zvz8p(ZP&FXO?@@VYisPUReo(5WwG{N#fE`%(c0k-S_UK_ma=?vzSy9zv2SgulZV{!
zDt~LQ)mxGT5ZSUzO|yLR7yp@=f6Y~-8%*fJxa|>id#mUHS_X#-QUcsy2X1F)cM-qk+Zh>Gy7MHrjUTKo@BU*s;P705r`rlQi^{lT
zvnVY=$jkWU5y--=>j#4t1dQ15>EvS=F+I|RMzM4yOKq_5A=%I)DeG?zM8b9U&eDO+
z@fnfxe{5_OTWix|locSR@C-_XSfyC+7tQ{i-r+iQJ+92l?=eIouIsR=fXE#fu?rm#
z=+my%t`1#)Tkh2dcqb+3i+zk7T2XafeS$VigAeDfr-FkET$9q9N%-J4CdFB`S&%}0
z(q9*$TWyj@zxwkl+o}04a8I8Mr@szarumAIfnxk4)D173Da;p|W3q`sHZ9EeCTjk^
z#^@ao*s(W$7+P`2aw37WWtF%!J_{X?!RlR1gvO(ot-i|&I$NIqNeWK{B=+@f{nEOj
zc^*4n2GHR-IepS+)z+ERmGBX+eSn>UO>K4;Tj%Phj*o?hA3odyc|@qBGQy6~Gzm7A
z$^C%3(LsOY^;H5_zm_l_2q#gNe!Rk(1nISA2kwI$_#vh3X|*tlqbSD(+{T*SUW;pb
zs?aGO_S{9raAfHl$k181e#(7a1x8bI_;f6bP8d6u4JfHERZ%#cW8pY{#o=m`=S&Qcw_S-dcihMEbvLJa-6X1{N!%w;-pM
z8;^jv+gQb4eLgO1LX<`o|MkcEOG{?cO}*AQ%EOiYX=KVUl7Jz`;S#C~aYeuzNl
zz|z9v&8iB)>cemx9!>0UALc`?gCL&(Vp^`>$i>z^GAnQ$*q2D{0~oqXAvydd3}qlk_VbcuPt9%ZwHGZdo)@U
zG0>MFHS%Ri58tN@T{>}0*Za~f%eUdx2T{x-Z;8K?o7WJ3ftQ|Y^oA#H-Af28Z-
z5H-w5J|##mLKL4AjA--!y^>8$U_+a*XuH5AjD3l&CJpk9>q=bxuQx%_-N|Q74Inld
z4JxG9ML%(_wFqz%B*8Y#@=bOH3;e;24CUanLEXiF;wH4kXnG^|4R?+Tmig0
z_WVW=8v4Aps4HFzhTU)e!k&OS4qXTm_{*~G-JfKOSEobeRiPrGZ((84;kdT%1-YGH`S|8u;QnR(6ac0DVW`fq7UisG#L!SR@JMP1
zWMv3E&jEi3vhLg0kA(jNdzlolh93Kf%iGm89#Cyb&gqAa}sL^v9qefJi0NU
z{QCp}b(U2y5|rX%feo8frKZ3P+sctY9jX;E&?Bkf^MkRu;Xj*OB;3eaF^X#Ys3(Ld
zv_@(I=+#4%yUcEs;f_JGi?ohXfTfRWX-LdY77A@n{VJe+p1)iTL&Idg$M`*b+`dn{nq+<|rD7-}RlYRb5jR1*Qa1B2M&xpUf
z?1q}}!_F*a-8iIoO~tsxy{LAxhWcadX=&-xsyiC-7|dBLy(Hn3(LoFc^#u7I(&q~D
zSD7B2qoI?ZSs=HUe|idggo4Y0RtmcrLz;hvgktahhuA5xy_crzc+WSUSL;f=Xsx0>
z>JsjlKjhp~F#Ud-wr2eEu&wVpdAQD0cM|=o_JebngVpy?YcoNR5{`!sgO%MG@?9h!
z`?>c!xoZQMZVXCR0>H>E%T_fHK(s)-jk@yL$>H7CH1bxaef9dwpC%bx
zlF>T7y-`_P`}q}ZEg&}90BorOT1kcuWgbs^VzpVn>oXmjpl)R$
zN3<HQ=YZ)$OWBkaEFRm+0Y+gx52qg
z!WOwML|uA*MZ++3H?aM`BKeXdZWHT>)ptI`b}(RAfev*O1frDgO&}bP$!O8ixZ9#!
zwsnvA;pc*{kq}=vbjSq!`6fmFaR4Kh!OT7kVMp_+MN-Ln)ENW}-qLk90=$ry7Bu$P
z2#6wODg5p6=^!$UL`9o7%1$xN10|InD90CTEE4sgovd&*!S%}-R+$1Lq0k1XEaI8z
zIE;c7$V!-7>{-@4Xr7lMlegTiTy!&A+TaR$4JK0zmVk~q`*mf)81r8w()q(VJj5qnVUH&0CCN&{90X(8;OSPUtTuALH<0`_IIkLaEsNbZ
zYG^_iUg$ibVV#N%3Xp{FlTp%w40nEhesR+IH4{1;@QO6@Y(yc&Jd+XQ^Hz^ywa7}g
z?LKlg^IQWQ&EZb5o@1-BU
zTOvl0&51wdu~`zh1d0)Zpf&2bk6Fh{Hly&jvQK~x=*Q2Qk9{gTZkR&zHb>Xs
z`IRmMIHp&8`0@aWZG~m~+3Vq)g@N1H5F|LQKA4HEvk@%mIo^@zr|A?N+1)&sr}z4n
zqg6`BcgVwDE?7Wd;$O2&_g0PVLaJeK!|B6)Ya2chwC?RMZ;U==N3ubE-Z^%9Er}|R
zk|Y3uat?Q+VgZ7GLs$wcORU)1b45vw-lcPE2_SgVH{=t}JAg$o?8>*-ur}GOuSyTn
zvm)5Y%uFHc>-P5c-Fqn3I(|?`79}yh#I^@mHpl?d>f4_h)2+|{uH?}b7Eh0UxBxevTodJRN37oDf
zhias^e0f>L{SF2i+#GtW)|mmluY*9VQqvQ~#A|F!f3TbMm?we=>S#;Vg18Xs0A#7X
zX-f{3&Rz@MInDhNSe(c&MGHMPTWGf7x5ch#B
zodnbFqgRNJ8eY|)PKCWlS<&GMqB)6P9|;oXLuc?b>h2wOv_<^3!-M^6+g&5ls(dWE
zE-FBx@fuYt43`C<^5<(coR7;{8hrrFpuICvODL!Ue?hVL7L1oeIr1S1Z~^X^SFL%j
zuwz@0F^`Jcx2%!w+sF^lB;tjV!xpX4{f8yfV`C2@WO^Y>(=ibb-g?+$_JjW+`A+5pC-pBc5+=6~D<2bKw83HJat
zIoM?@0+CL?->*N|7$XQ7ft!H#5=y%}@8JZt=92`FGJe!faK^JghTSQD4p|I92IpPG
zEj+IrP4b`;jd#UvS@Rt*{{HSHALrgof_jayyBG|Jxt3+W#8|R%Z`?LHXpDsiF*r8)~-Mc=3X^Dydc;sg^b@e?&
z8$bGSU?2*yifqIiB1Yx5g%-#)`tN3Q#|~4pd!+li1oCNrLI~mp-oF5&H&6!RaSNhi
zR-x9w6rx+2OrVKb%y7^EW9^C>wRq=+DOV5@4Ko}UwnJBG$rO%MjVzRsyVAt&H
zc=8>B%j%IEfd^tbK*gBpF{iTYH`@q{DW{yNZAkA5H4|0+?)}<%L6&e}0VoVC;0v2s
zzJDS3_YYI-ce{;x3A~~Qe;>DhZ3v4beQBf$A&!4FG3eeJ@
zz$);6VI2Koj0qN99bE4$f^s7(E-XR_E$~-x2;~ORaJAq1bt_
z^uue9USKU%?`o5FFIL5$p4yw7n%eDn>&BWpnoa&O+?Qljmoks4W%7SS9n8U7^8`AG
zP&lZ(5xF_-4x{GB=k@^c*al9wi4IbT%eGJx-znOba*07r%SMvtQZ5ho{}A@(fmH9`
z`uNGA(5WO+q9jy?%t@5pK!nnuB9d8&LKIQ$hEo)YGFM76
z%9k(CyqclW5MRzXK7}17Y9_Vg(8XyWOl6c;eB?okr;Vqa4TPk8Vq>&yF5~7=pXD*o
z*LedeZE5M|{gknDhptzk7e~nrHAwpP2T;{dM{M1BSt%iIptlFjS`I@`ZL2c_m{pEQ
z7Ux{hR5Es4UKD?!yQ#ETd*4^sz7#1=y*uj<@*tvK7mL-_S=-IYN7BMxTtMiJJl*Nv
zXTED`B*5s`(6(|7i=8gA;`)=H1JuUdJSh%XleYa)wCDc_F!d0&>OE<~MzMfB(Slwl}J+vQ6+r<|cca`A}}&+W)k3
z6fi==gqPiZt`x`9+ZUm@OZ?LGS!
zPWvjcz*Dq69h%I0XCgyx-T&@m?+G#GGCy}5lZ^~W5mN);9(`-Rd+)sd
zo!-c!$zFJeSm$Yc8q+$_#Fi^6D;=K~1_cEf=xtL|_o&Knv`Bk_n2^#=OTFQNT0*s7
z-+$UO|G$r7XUoO2-c%agEVyYYzyucZ@q}YpIWthXMI60UCMaWVZmgX=xTz}r&>_P_+*og2!FQ#^W_%5myhy3
zNFEEIZCZCPA^b0A!6Td5wZH`WnzZgu#nt)#?WX1$%FGsCxF^e!GL@>DrSV=B_M!fB
z>lj`UWg3?uG*WubuDJe)cRnyAnv^1w{~Ru(4vV(6y}99moZmTkn+#pQ7ZwNDrR9E4
znsMz#2J&e`r*>u>lUsHAjkf(fE^_+L$(u5rOv#426L;id;IlXH+^Kzrp+NcM8|d;r
zFCcf5mqy72J-sZwqqSa01#h6A%Pl+)sTp9qs;i}AcuHkM+m`jgyW{ef#THJxKRjS3
zY9AWmdHfNh^R?vsZl0qFLNiFNu4r{zh0c>NM%K&mW!|)hd2zI?!!6~TSI)h7^M-I$
z!!4#Be@s!?8e4o1yya;T14&-XrBYSLzGCT(a?MfS_`}I{b?HP#c$|%H$IiPgwF>T2lVi1`a}Eq@LiY%dGQZCV~^=g=Bwa3aMsEFhBVi?0_eANhhw*o^83Xu
z-W_?{UnTXuYhEk{l)6+gU@!U*403$hTdwY1eULSZ=kc)#&jxOVXLmH>tg9Oj`e6AF
zD!?F&A<_DcZ8vdFE6+#xzzdPBQ|77sw^^?CIA1sX`@2e>H4FxcDoD4_sMXq&1Ouc_
za&tvP-HkhmDf{8~qY5j=@_6S}Ru_Oml_bN3xDz(fQb=H)$GaNJnSJ2_id0teSx`L4#Uah
z^EN=8oW;_w;X|VinArGFIsLrLW-%ojVvr!qWJZQvFq)|Px)W7*AzkvYE%7F3XJd>H
z4{rv)O!3L9qWt0O;O-;JpZQ@MkKfBY)rwNkqo-_JqTHi0*B*9{94s$9RKm+&}|2o+hb
zul;f@@9p)7L;iBQ@{D%{t2I$l@KTIFr_$vpxyk4jG6+k3nv5X
zu+}&7<=Ceut<_$1kyGkHQwSaC
zm}xOy+v2Jg7m&S#n{wM+JBBYu?d{9%?`|%Hhi_J?mlc}ZJf0iPc6+fwaE79)<>drw
z{%U%stQ~w{r5i&(p`tI(}VVbr8zi#-nzfDoa
zmc(aTSln@27O@&gpP2@8hJPIS&xyUe!Yq!^pn=Nid9VsVJ+|5*c-z1+R;r^x_$PRQ
zs9=)cLDo!lekE1Q-!6+VtuDXFY5INR46;)tpcSc#QFkMk#Ih(>!@FZDH==d+%##K%
zDP-tW-=qWEI8pVO?wKQ8vB!}{pKwvh@OCVZUqYV*-=(6dla5nfw(3um)J=p{rJKj^
z>-};AeNpaiPrKYP20C4bInF7
z1^^{HgfCugIdK=_#kS}B?o454E8^0N>X;x5P4d`iw>J^zbeYEb7Vkn&
zcCn=tswAxw_j-GBK%E54ySnO}rE)on_9tDBX4u}Az&7h-LzWY^_TCji1(fby?p;TYX2m#`fag;kL}Oz
z?U@BZSspkI+Bx5eTGF;3H%^AH@QQJy(;_$YbDbLX`)KSLZi15W#3gSTTjfG1N@|9q
z7_uS&|5m}42y+gdcgv(I7K7Wc~Y#$R)ZDvDhSe~{E-lq$}gp{TFNv-
z&pBB0vT8JkrZP0@HUdH_nF?3~i59}I^n1le4p+NRDCcb&c2Ht?7|Pvt(?Qpg*{pL$
z@lHQkal1bD>A<~`X>Htj0GPIQ986`kb>MI{q(RPkah?8b@7BtE(BJ30EGmI>=aYqC
zhguyD?d;GrR@CLqPP=t9W-yj4NEIors3>emIZkGaiMengdAznhUtG8S=8H?lk@GBa
zdQqVjz#xnZbc(NbH3>RQ77vtEX}=&h*0AbZs?CpqJuhM3Y-DTgNw|C0uY0o|;}^2x
zxj|yruF1+pr_7!3={Z{&wj)`~R?ou>l6_S=!(OXztlj-AB}xbRxq#4l^zb5VC+sN%&t$fyX5
zE;+wQX9%A0T4cWVQhK9x;(`9i1BpLxG}O-CH*pBw1SKNhN&JvC%n7?l&Kvc#C;pIF
zpu+X8e&*2s3f
zeM~coB!+VkWI~K#^2W3fhrpt>I%M&m6@6%<+N`Hz(2bu}3h>NoPTI+)7-`H+^VN;LvhqSb`j97q`7vWT0*ZpnWodC7!
zFT^^dj@E3r8TR!0wQI-8Ehj8`1tvy66&X8iVUE|EbX%@AfQ)UZcrwzxwMux7EWr&N
z;e_!_ZwNcy9awDL^b1P4#zBW^px{X$H^!Sc`-dxz@k7q+xP`_$!nkMb=@LWA2NiAd
zbT_1InnGMVGU7yS{Q%LhDZ5)EG24Q5vxpQJYyR-NR$YW
zFfEzd|IGVq{&9o+7l3Rky>+y0xXS!5(vk~zAI}T;S#WUF@6uH(I*v?Qb(|O_GDIt|
zep7`#=wipd%pz--#XU*iXPPuJSB0!(ssX;jALIm$qR1aJb1uK!&rfdq+fn_4{;#MT
z+_ek~rm>z#w2rxgBU3)+RQkWc9d5XVyTo%$;zKdMFgbZ_PSgl(lP;og$oZDB?
zTc5;p_Uu{T3%KExPxCVF^qVnA1(Pdsh<;zWZY#Qce07F^5-x`xx>k1tg0j4ezGLT?
z5PINk>@PJrg%8-jue)H2#I6$)A+L_e{qAM$Z
zO^*q^CnubCr_nyXjIT%|fpyI3o=>Lr4`Z7jYu{n6L#fZ;+B;uS3>#lbhK)9B+N3GE
zdv_5GIe*8S&~6Uq=SWg~MLcgRVNB-1TSkb@;qZJ+GEcUHfvr+nBfU(i(20gYhp}dz
z(6Tm&BwoKBOhOz?D~whDAJ6lDokCvAEkN!5Sqv;VYu!(*yR>^=p+
znSB-My-<@5-ci?;<0EiGW1)e{R=Hh`M~J`9|Buk%x^8OozkdVw+gPy>8thPC_IU~Z
z&+-hKj99YoF$&eD($kEH$S58q1t5kQwO=4NX3w9M4orOonEbUDoN?U9tM6~Rlwko~
zEbu51Jw94kv72)tPt8vJ_)UdzBL%XKt@SUPf!n?l>{6ykPUftk7H4js;RGMuT#`lN@1%*X&nJvqWTo!*!7|+EKn697|Se1PB$$oGcV~0&j
zf8xR!7me;ih8H1)(+v$R8U~K>k-3`7>v@9*WYGd}96F7*uZ!YFC@oACLWk1sn(kBH
zNQg0ezFav{{3&oLYgs3o|B%GNBOb$I0W54>mJThfDFu1c@yW-SV43)Fv;{ss>~W!Y
zCh!kNuT&x0%k(7p6+DPlV_9KbU$r~9xV);5Gb|szb_!Lq9_zo+FmG8Il)urpZh;;3
z^M^@iAGi7?PU`2S^l&Fx`uh43uukw*Rz-K$$bq|`Z=uGp(z}Z{c*s^<7t?E1J?fs|
zAC94u9GrW(8zH^?G7m>Bfu=~cXd+(MVUnBih*)To7!*48_rT*AelLr`gx;$vw*cE1
zzC}0@;;3EVBttXy%sl%~t>&FWcL1(zg6J{D=Kf-^R{W_}cho5JKs6D3c|jBvsK6R0
zQ-e|Mm#SkUM}DF}CN}}mDnMN-MhJi|+VKQ;Tbv{SB0iO1B8SlfP_JKDm08Yck}-L
zqHuW=HtYLo&&iGJB-x{vj!PXw9VW(t>o;s@$~_mr-z{v!xY1deW6umuKf(#yL#%w}
zGOfmO7_W7Ku)o-@uvF^tG8p?$z^v%?UuKO@?+j`9z|*n@t%&sniasBpUtq>)Fyc%(4*U$BCGx*Uwok=;Vf!
zS0?}N!^2|=L3~d`MIMAOQ2;(VqDOoNj9a-N#FI*}t;P|GoO@wjs2#0#*?MB=Rs^e5
zJ_YExo$wSj2gCol-!eDDxhw=am*pr8JewGP1N|yiw;<(mP+bR8WhA;7Rzlz(7>y1Oxgb5InoOS@2#-H$0eQ^-8eBt~Oy<`I|dNnz3ruq+gV
zinx?0=HJ+tCZdU|;*X;tzb*y-i_~8@WUFG1!^uf)sK;hla~-dV(Fsb9UHj1YcbrTV
zU3dH}C@L!2iw(pr$twr>!UKf$dJNFBTJ^ggE~m?Qpf8wn{VyaTCQIR9ZzZB7EGFGB5+)bK)Fpy-f+E0`~_)b
zD<{u!82u3_*SohhyALq#+%s8RL7A+Mmn3SU6EL{a+PJ{=C%pV3vIc>`KYe>XtrGyz
zaVX$k8XNxzK+vgleNA_tozN$Q3gn2hvJ|k31A#b=eED@lfymwk;ziq>3TUUAF)Gah
z9ekcj$&mxI`Y`%e{Sy^(1Xf!
z2yb$=aa1{n?}cq^>C|G;Hz4%9j@ofiS{H*#%C`q18Ef#y4CH4_vJhEdw)etv>5WL|}
zwAs<+-G7Ys`i~zwm7Sp^bX7_9?ksiwV?gR8b$m*GWW`{DXyx~%;uJaxAW~kd+=I@B
zKnUqy{{5mWCqK<<@T}QCSE%yU*Yk{sr;-B6UQv=ao~b>}V+&-?j+Cou+8|X^+K*}g
z^VTFwcfUkP`%aQoUC&!hJZXFuw-0#FjMso%o&Egm=`aCN6Yab>CPxibkK`-PQ3#(xVEFiHL2pbKo-5w(K
zAMfKoR#D>=?L%ar(d%|&ez3OvHa0Y~gaW2mWIkVI8cf3|Eb7TEtvuM}L1uyeWG|3Q}PSn%yTN;>i@_b={cZtk2r
z7KcD(V};Vx5=kYY@l(}gn)h5(9TpnmDrdi72KyyQt++fpujM-Avey$kYX9RU{qNQ^
z6vJYC1Pn6+QqSU~Zm1ue-JZRM=F}2`ea-8te2=Vtin+hNId{W#%dEQlJzThAl0F_o9+K|e`ja}Oy)MXW=cG}<|&N;_AsasgJ!8Y>`k{{r2m_x8l<`g)G2xh=oR4R2FUE&lN-E=KF!u4QGL
zEU6r}cnk~7PI!u|x+>b#BaVk@I#RoZPkWIcf973$N&EshS8S4~kH^4;|9vh0?*@9a
zB|atD7`Q%3fk^h$p4+v5aYN`b&n9%s&2(1WDE0aA@KDFF(>U-WUo5IMsW_g(D_C`m
zblp?#CQV@c+&Ky))2D&tr<0#%d(;;!z_{Q#EZ@#@G3o29K6I|-6QDENx`Tk;vH^!v
z%B1)YGDrWCo#fO(#(e)B;U-bg4&*EES8d+26>Hj=xH>>S51xeuc!F5