Worker Displacement in Japan and Canada
Bibliographic record
Abstract
Non-Employment Spells after First Separation by Seniority in France 5.7 Weibull Proportional Hazard Models for Return to Work for France 5.8 Non-Employment Spells after Separation by Seniority in Germany 5.9 Cox Models for Return to Work for Germany 5.10 Two-Period Earnings Growth by Seniority at Date of First Separation in France 5.11 Two-Period Log Earnings Growth, by Seniority at Date of First Separation for Germany 5.12 Log Average Real Daily Earnings Regressions for France 5.13 Earnings Regressions for Censored Regression Models for Germany: Displaced Workers Only 5.14 Earnings Regressions for Censored Regression Models for Germany B.1 Type of Closures for France and Germany D.1 Sample Statistics for 1984 for France E.1 Separation and Censoring in Germany F.1 Sample Statistics for 1984 for Germany G.1 Probit Models of Incidence of Separation by Type Relative to Continuously Employed in France in 1984 H.1 Weibull Proportional Hazard Models for Germany I.1 Constrained Earnings Regressions for Germany: Displaced Workers 6.1 Labor Market Characteristics 6.2 Macroeconomic Environment in Belgium and Denmark 6.3 Incidence of Displacement among Private Sector Workers in Belgium and Denmark 6.4 Characteristics of Displaced Workers with Tenure of at least Three Years in Belgium and Denmark 6.5 Factors Affecting the Probability of being Displaced, Compared with Nondisplaced Workers in Belgiuma and Denmark 6.6 Unemployment for Long-Tenure Displaced Workers in the Three Years after Displacement 6.7 Reemploymenta after Displacement in Belgium and Denmar 6.8 Duration Analysis of Reemployment for Long-Tenure Workers in Belgium and Denmark 6.9 Average Annual Earnings and Earnings Growth for Long-Tenure Workers by Years after Displacement 6.10 Average Wages and Wage Growth for Long-Tenure Workers 6.11 Regression Analysis of Wages in Subsequent Job xiii
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".