Sensibilité du chômage et caractéristiques de l’offre et de la demande sur le marché du travail
Bibliographic record
Abstract
In this article, we look at the impact of both supply and demand of labour on the determination of the unemployment rate. The analysis is conducted with annual data by 12 demographic groups over the period 1956-1978. All the data prior to 1976 from the Labour Force Survey were adjusted to the definition of the new Survey. A few differences in the reaction of demographic group unemployment rate are noticeable: (a) in general, male unemployment rates react much more to demand side evolution while female rates are more influenced by movements in the supply of labour; (b) some groups clearly exhibit "discouraged worker" behaviour when facing adverse economic conditions, (namely youths, women aged 25 to 34 and older men); (c) due to a lower sensitivity to economic conditions of many of their jobs, adult women present a fairly stable situation of employment, so their growing unemployment rates are the result of higher competition stemming from an increasing desire to participate in the work world; (d) together these findings have some implications for government policies; more specifically, they emphasize the limitations of demand policy and point to the need for selective measures and a diversification of job opportunities for women.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".