axis = SocialNorms/Care (Authority degenerate); over-steer generation

scripts/diag_axis.py shows steering at 1 nat moves gemma's foundation profile
the right way: SocialNorms 0.68->0.42, Care 0.21->0.33, coherence 0.72->0.88.
Authority is ~0 on this model (no headroom), so:
- eval reports all foundations; trait axis = SocialNorms (down) + Care (up)
- map.html plots Care vs SocialNorms
- add gen_alpha=1.5: over-steer generation into the incoherent regime so the
  heal (Q1) has work to do (at 1 nat coherence improved, nothing to heal)
- results.py groups on coherence/socialnorms/care

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>

scripts/diag_axis.py

@@ -0,0 +1,51 @@
+"""Diagnostic: does the steering vector move the moral-foundation profile, and where?
+
+Base gemma-3-1b-it puts ~0 on the Authority foundation (forced-choice), so the
+"authority axis" has no headroom. This prints base vs steered (at calibrated
+c_star) 7-foundation profiles side by side so we can pick the axis the trait
+actually moves. Run: uv run python scripts/diag_axis.py
+"""
+
+import sys
+
+import torch
+import tinymfv
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+sys.path.insert(0, "src")
+from steer_heal.config import RunConfig  # noqa: E402
+from steer_heal.steering import teacher_vec  # noqa: E402
+
+MODEL = "google/gemma-3-1b-it"
+cfg = RunConfig(model=MODEL, n_prompts=12)
+
+tok = AutoTokenizer.from_pretrained(MODEL)
+if tok.pad_token is None:
+    tok.pad_token = tok.eos_token
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL, torch_dtype=torch.bfloat16, device_map="auto", attn_implementation="eager"
+).eval()
+
+v = teacher_vec(model, tok, cfg)
+
+
+def profile(label):
+    rep = tinymfv.evaluate(model, tok, name="classic", n_vignettes=24,
+                           conditions=("other_violate",), max_think_tokens=64, device=model.device)
+    p = dict(zip(rep["profile"]["foundation"], rep["profile"]["model"]))
+    p["_coherence"] = rep["mean_pmass_allowed"]
+    print(f"\n=== {label} ===")
+    for k, x in p.items():
+        print(f"  {k:12s} {x:.4f}")
+    return p
+
+
+base = profile("BASE (c=0)")
+with v(model, C=v.cfg.coeff):
+    steer = profile(f"STEERED (c_star={v.cfg.coeff:.1f}, ~1 nat)")
+
+print("\n=== delta (steered - base), sorted by |Δ| ===")
+keys = [k for k in base if not k.startswith("_")]
+for k in sorted(keys, key=lambda k: -abs(steer[k] - base[k])):
+    print(f"  {k:12s} {base[k]:+.4f} -> {steer[k]:+.4f}   Δ={steer[k]-base[k]:+.4f}")
+print(f"  coherence    {base['_coherence']:.3f} -> {steer['_coherence']:.3f}")

scripts/results.py

@@ -19,13 +19,14 @@ df = pl.read_csv(RESULTS_TSV, separator="\t")
 agg = (
     df.group_by(GROUP)
     .agg(
-        pl.col("p_ans_any").mean().round(3).alias("coherence"),
-        pl.col("auth").mean().round(3),
-        pl.col("auth").std().round(3).alias("auth_sd"),
+        pl.col("coherence").mean().round(3),
+        pl.col("socialnorms").mean().round(3),  # trait axis: lower = more trait
+        pl.col("care").mean().round(3),
+        pl.col("care").std().round(3).alias("care_sd"),
         pl.len().alias("n"),
         pl.col("seed").cast(pl.Utf8).sort().str.join(",").alias("seeds"),
         pl.col("argv").first(),
     )
-    .sort("auth", descending=True)
+    .sort("care", descending=True)
 )
 print(tabulate(agg.to_pandas(), headers="keys", tablefmt="pipe", floatfmt="+.3f"))