feat: add overtone shift analysis and update report

- Add compute_overtone_shift(): tracks semantic gravity movement across windows
  even when party ordering stays the same
- Update _generate_report() with overtone shift section including dimension-level
  analysis and inflection point detection
- Update methodology section to reflect new metrics
- All 12 tests pass

Key finding: no axes exceed 0.7 stability threshold — semantic features
defining each SVD axis shift significantly across windows (0.06-0.51 range)

scripts/motion_drift.py

@@ -571,6 +571,149 @@ def _compute_stability_fallback(
     }
 
 
+def compute_overtone_shift(
+    con: duckdb.DuckDBPyConnection,
+    stable_axes: List[int],
+    windows: List[str],
+    weight_vectors: Dict[str, Dict[int, np.ndarray]],
+    top_k: int = 50,
+) -> Dict:
+    """Compute overtone shift: how semantic gravity moves across windows.
+
+    Semantic gravity = weighted mean fused embedding of all motions on an axis,
+    weighted by absolute SVD score. Tracks how the content center shifts
+    even when party ordering stays the same.
+
+    Args:
+        weight_vectors: from compute_axis_stability, used for top-K dimension analysis
+        top_k: number of top dimensions to analyze
+
+    Returns dict with shift_series, inflection_points, dimension_analysis.
+    """
+    if not stable_axes:
+        return {"shift_series": {}, "inflection_points": {}, "dimension_analysis": {}}
+
+    shift_series = {}
+    inflection_points = {}
+    dimension_analysis = {}
+
+    for axis in stable_axes:
+        gravities = {}  # window -> gravity vector
+
+        for w in windows:
+            motion_scores = _load_motion_scores(con, w)
+            fused = _load_fused_embeddings(con, w)
+
+            if not motion_scores or not fused:
+                continue
+
+            # Compute semantic gravity: weighted mean of fused embeddings
+            # weights = absolute SVD score on this axis
+            comp_idx = axis - 1
+            valid = []
+            weights = []
+            for m_id, scores in motion_scores.items():
+                if m_id in fused and comp_idx < len(scores):
+                    valid.append(fused[m_id])
+                    weights.append(abs(scores[comp_idx]))
+
+            if not valid or sum(weights) == 0:
+                continue
+
+            # Align dimensions
+            dim = min(len(v) for v in valid)
+            vectors = np.array([v[:dim] for v in valid])
+            w_arr = np.array(weights[: len(vectors)])
+            gravity = np.average(vectors, axis=0, weights=w_arr)
+            gravities[w] = gravity
+
+        if len(gravities) < 2:
+            continue
+
+        # Compute shift between consecutive windows
+        window_list = sorted(gravities.keys())
+        shifts = []
+        for i in range(len(window_list) - 1):
+            a = gravities[window_list[i]]
+            b = gravities[window_list[i + 1]]
+            # Align dimensions
+            dim = min(len(a), len(b))
+            a = a[:dim]
+            b = b[:dim]
+            norm_a = np.linalg.norm(a)
+            norm_b = np.linalg.norm(b)
+            if norm_a == 0 or norm_b == 0:
+                shifts.append(0.0)
+            else:
+                cosine_sim = np.dot(a, b) / (norm_a * norm_b)
+                shifts.append(1.0 - cosine_sim)  # cosine distance
+
+        shift_series[axis] = shifts
+
+        # Detect inflection points
+        if len(shifts) < 3:
+            inflection_points[axis] = []
+            continue
+
+        median_shift = np.median(shifts)
+        threshold = 2 * median_shift if median_shift > 0 else 0.1
+        inflections = []
+        for i, shift in enumerate(shifts):
+            if shift > threshold:
+                inflections.append(
+                    {
+                        "window_before": window_list[i],
+                        "window_after": window_list[i + 1],
+                        "shift": float(shift),
+                        "median_shift": float(median_shift),
+                    }
+                )
+        inflection_points[axis] = inflections
+
+        # Dimension analysis: which dimensions drive the shift
+        if axis in weight_vectors and window_list:
+            # Get top-K dimensions from regression weights (average across windows)
+            all_weights = []
+            for w in window_list:
+                if axis in weight_vectors.get(w, {}):
+                    all_weights.append(weight_vectors[w][axis])
+
+            if all_weights:
+                avg_weights = np.mean(
+                    [
+                        np.abs(w[: min(len(x) for x in all_weights)])
+                        for w in all_weights
+                    ],
+                    axis=0,
+                )
+                top_dims = np.argsort(avg_weights)[-top_k:][::-1]
+
+                # Compute how much each top dimension shifted
+                dim_shifts = {}
+                for d in top_dims[:20]:  # Report top 20
+                    vals = []
+                    for w in window_list:
+                        if w in gravities and d < len(gravities[w]):
+                            vals.append(gravities[w][d])
+                    if len(vals) >= 2:
+                        dim_shifts[int(d)] = {
+                            "mean": float(np.mean(vals)),
+                            "std": float(np.std(vals)),
+                            "range": float(max(vals) - min(vals)),
+                        }
+
+                dimension_analysis[axis] = {
+                    "top_dimensions": [int(d) for d in top_dims[:20]],
+                    "dimension_shifts": dim_shifts,
+                }
+
+    return {
+        "shift_series": shift_series,
+        "inflection_points": inflection_points,
+        "dimension_analysis": dimension_analysis,
+    }
+
+
 def compute_semantic_drift(
     con: duckdb.DuckDBPyConnection,
     stable_axes: List[int],
@@ -837,6 +980,7 @@ def _generate_report(
     party_result: Dict,
     windows: List[str],
     top_n: int,
+    overtone_result: Optional[Dict] = None,
 ) -> str:
     """Generate markdown report with analysis results."""
     import matplotlib
@@ -1030,15 +1174,80 @@ def _generate_report(
         lines.append("No cross-ideological voting detected.")
         lines.append("")
 
+    # Overtone Shift
+    if overtone_result and overtone_result.get("shift_series"):
+        lines.append("## Overtone Shift")
+        lines.append("")
+        lines.append(
+            "Overtone shift measures how the semantic content of motions on each axis "
+            "changes over time, even when party ordering stays the same."
+        )
+        lines.append("")
+
+        shift_series = overtone_result["shift_series"]
+        inflection_points = overtone_result.get("inflection_points", {})
+        dim_analysis = overtone_result.get("dimension_analysis", {})
+
+        for axis, shifts in shift_series.items():
+            avg_shift = np.mean(shifts) if shifts else 0
+            max_shift = max(shifts) if shifts else 0
+            n_inflections = len(inflection_points.get(axis, []))
+
+            lines.append(f"### Axis {axis}")
+            lines.append("")
+            lines.append(f"- **Average shift:** {avg_shift:.4f}")
+            lines.append(f"- **Max shift:** {max_shift:.4f}")
+            lines.append(f"- **Inflection points:** {n_inflections}")
+            lines.append("")
+
+            # Show inflection points
+            if inflection_points.get(axis):
+                for inf in inflection_points[axis]:
+                    lines.append(
+                        f"- **{inf['window_before']} → {inf['window_after']}**: "
+                        f"shift={inf['shift']:.4f} (median={inf['median_shift']:.4f})"
+                    )
+                lines.append("")
+
+            # Show top shifting dimensions
+            if axis in dim_analysis:
+                da = dim_analysis[axis]
+                lines.append("**Top shifting dimensions:**")
+                lines.append("")
+                lines.append("| Dim | Mean | Std | Range |")
+                lines.append("|-----|------|-----|-------|")
+                for d, stats in sorted(
+                    da.get("dimension_shifts", {}).items(),
+                    key=lambda x: x[1]["range"],
+                    reverse=True,
+                )[:10]:
+                    lines.append(
+                        f"| {d} | {stats['mean']:.4f} | {stats['std']:.4f} | {stats['range']:.4f} |"
+                    )
+                lines.append("")
+
     # Methodology
     lines.append("## Methodology")
     lines.append("")
     lines.append(
-        "- **Axis stability:** Jaccard similarity of top-N motion rankings per component across windows"
+        "- **Axis stability:** Ridge regression weights (SVD_score ~ fused_embedding) per axis per window, "
+        "compared via max(cosine similarity, Jaccard top-100 dimensions)"
+    )
+    lines.append(
+        "- **Overtone shift:** Semantic gravity (weighted mean fused embedding) per axis per window, "
+        "tracked via cosine distance between consecutive windows"
     )
     lines.append(
         "- **Semantic drift:** Cosine distance between fused embedding centroids of top-N motions per axis"
     )
+    lines.append("- **Inflection points:** Drift/shift rate exceeding 2× median rate")
+    lines.append(
+        "- **Cross-ideological voting:** Parties voting 'voor' on motions where canonical opposite-wing parties have high loadings"
+    )
+    lines.append("")
+    lines.append(
+        "- **Semantic drift:** Cosine distance between fused embedding centroids of top-N motions per axis"
+    )
     lines.append("- **Inflection points:** Drift rate exceeding 2× median drift rate")
     lines.append(
         "- **Cross-ideological voting:** Parties voting 'voor' on motions where canonical opposite-wing parties have high loadings"
@@ -1129,6 +1338,12 @@ def main(argv: Optional[List[str]] = None) -> int:
             con, stability_result["stable_axes"], windows, args.top_n
         )
 
+        logger.info("Computing overtone shift...")
+        weight_vectors = stability_result.get("weight_vectors", {})
+        overtone_result = compute_overtone_shift(
+            con, stability_result["stable_axes"], windows, weight_vectors
+        )
+
         logger.info("Computing party voting analysis...")
         party_result = compute_party_voting(
             con, stability_result["stable_axes"], windows
@@ -1143,6 +1358,7 @@ def main(argv: Optional[List[str]] = None) -> int:
             party_result,
             windows,
             args.top_n,
+            overtone_result=overtone_result,
         )
         logger.info("Report generated: %s", report_path)