Comments on “An Empirical Assessment of the Employee Free Choice Act: the Economic Implications” by Ann Layne-Farrar
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it