Bibliographic record
Abstract
American employment policy for displaced workers started in the Great Depression with programs for the employment service, unemployment insurance, work experience, and direct job creation. Assistance for workers displaced by foreign competition emerged in the 1960s along with formalized programs for occupational job skill training. The policy focus on displaced workers was sharpened in the 1980s through the Worker Adjustment and Retraining Notification Act and the Economic Dislocation and Worker Adjustment Assistance Act. Field experiments on services to dislocated workers led to Worker Profiling and Reemployment Services systems in all states, and federal rules adopted as part of the North American Free Trade Agreement Act permitted UI benefit receipt while starting self-employment. Evaluation evidence suggests there should be continuous connection of unemployment compensation recipients to reemployment services, skill training closely connected to employer requirements, earnings supplements to ease transitions to different jobs, efforts to maintain and strengthen employer-employee relationships, information channels to employees and communities about impending employment disruptions, and targeting of services to improve returns on public investments. While no silver bullet emerges to solve worker displacement, many different programs addressing a variety of needs can improve labor market outcomes after permanent job loss.
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".