Worker Displacement in France and Germany
Bibliographic record
Abstract
Book description: \nThis volume presents a collaborative effort by 22 labor economists who examine worker displacement and the attempts to address it in 10 industrialized countries. Using large nationally-representative data sets and detailed policy analysis, the authors focus on two key questions related to worker displacement: 1) whether the experiences of displaced workers in the Untied States, and the patterns of experiences across workers, echo patterns seen in other developed countries, and 2) what can be learned, both from the similarities and from the differences across countries? \n \nFor instance, do commonalities in displaced workers' experiences across all countries reveal fundamental features of modern industrialized economies? Are international differences informative about the efficacy of different public policy approaches to worker displacement across countries? \n \nWithin-country patterns are described using a number of demographic characteristics including age, tenure, gender, and skill level. Results are also offered from cross-national comparisons in the levels of key variables (such as the frequency of displacement, and the duration of post-displacement unemployment) and the association of these variables with international differences in labor market structure. While these sorts of results are generally the most difficult to generate, they are potentially the most rewarding. And in the case of these efforts, they are thought-provoking as well.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".