Publication Bias in Union-Productivity Research?
Bibliographic record
Abstract
This paper develops and applies several meta-analytic techniques to investigate the presence of publication bias in industrial relations research, specifically in the union-productivity effects literature. Publication bias arises when statistically insignificant results are suppressed or when results satisfying prior expectations are given preference. Like most fields, research in industrial relations is vulnerable to publication bias. Unlike other fields such as economics, there is no evidence of publication bias in the union-productivity literature, as a whole. However, there are pockets of publication selection, as well as negative autoregression, confirming the controversial nature of this area of research. Meta-regression analysis reveals evidence of publication bias (or selection) among U.S. studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.382 | 0.629 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".