Comments on “An Empirical Assessment of the Employee Free Choice Act: the Economic Implications” by Ann Layne-Farrar
Bibliographic record
Abstract
“An Empirical Assessment of the Employee Free Choice Act: The Economic Implications” by Ann Layne-Farrar provides empirical evidence concerning the impact on the U.S. unemployment rate and employment-to-population ratio should the highly controversial Employee Free Choice Act (EFCA) become law. The paper has received widespread public attention and its analysis is being used in the debate surrounding the EFCA. This commentary raises three important questions about the empirical analysis: Are the predictions presented in the study, concerning the effects of the EFCA, realistic? Is the research design likely to identify the effects of the EFCA? Why do the data used in the analysis cover such a short time period? The discussion suggests the empirical results presented in Layne-Farrar (2009) should be viewed with considerable skepticism.
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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 | 0.021 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.039 | 0.040 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".