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Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review. How this is built →

Methods & evidence

A reader-facing audit of how strong the evidence base is: study designs, paper-level evidence strength, and claim-level grades.

1699
assessed papers
19.5%
causal-design share
16496
classified claims

Study design mix

Assessed papers by paper type.
DesignPapers
Descriptive484
Theoretical347
Quasi Experimental238
Review Meta233
Correlational222
Rct93
Commentary55
Other27

Paper-level evidence strength

Assessed papers by evidence-strength label.
StrengthPapers
Medium818
N/a419
Low415
High47

Claim-level evidence grades

Extracted claims by evidence grade.
GradeClaims
Medium8413
Low2961
Speculative2091
N/a1545
High1485
Medium-High1

Highest-weighted evidence categories

Average evidence weight combines study type, evidence grade, and extraction confidence. Categories with fewer than five weighted claims are excluded.

Outcome categories with the highest average claim evidence weight.
Outcome category Claims Papers Average evidence weight
Team Performance 176 83 0.263
Creative Output 70 30 0.261
Task Completion Time 272 194 0.258
Worker Satisfaction 220 115 0.249
Output Quality 896 347 0.247
Wages & Compensation 185 98 0.237
Decision Quality 740 319 0.228
Error Rate 210 128 0.228
Labor Share of Income 57 35 0.227
Innovation Output 401 210 0.211

Caveat

The causal-design share is a coarse screening label from automated assessment. It is useful for triage, but individual papers still need human review before making causal claims.