Employment and Productivity: Disentangling Employment Structure and Qualification Effects
Bibliographic record
Abstract
Based on a disaggregation of the workforce into three qualification or educational attainment categories, the article estimates the effects on hourly productivity from changes in the employment rate structure and from changes in the qualification structure. 21 OECD countries are then ranked in terms of the potential gains in GDP they could expect from moving to the educational attainment rates and employment rates of the best performing countries. RÉSUMÉ S'appuyant sur une désagrégation de la population active en trois catégories de qualifications ou de niveaux d'instruction, cet article estime les effets sur la productivité horaire de changements de la structure du taux d'emploi et de changements de la structure des qualifications. Nous classons ensuite 21 pays de l'OCDE selon les gains potentiels de leur PIB auquel ils pourraient s'attendre s'ils atteignaient les taux de niveaux d'instruction et les taux d'emploi des pays les plus performants. WHICH EMPLOYMENT-BASED POLICY would
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".