Can a work-readiness program mitigate unemployment scarring: the case of a co-operative education job market
Bibliographic record
Abstract
Using a two-year-long longitudinal dataset that follows co-operative education (co-op) students’ employment situations, this paper examines how unemployment in the first scheduled co-op work term is associated with un(der)employment in subsequent work terms. Drawing from unemployment scarring theory, the paper also investigates the role of a work-readiness intervention in reversing the potentially negative consequences of unemployment in the first scheduled co-op work term. The results suggest that unemployment in the first co-op work term is associated with underemployment in a subsequent work term. Compared to those who were employed in their first scheduled work term, students who were initially unemployed were just as likely to be employed in their second work term, but they got jobs later, were in jobs with lower seniority, and were paid less than expected. By the third scheduled work term, employment and underemployment were similar between all groups, except that initially employed students continued to earn more, suggesting an earnings penalty for initial unemployment that is consistent with unemployment scarring theory. Critically, participation in a work-readiness intervention reversed this narrative. Intervention participants did better than their unemployed peers in subsequent work terms, and their employment situation was more like that of the initially employed students.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".