Bibliographic record
Abstract
Cet article porte sur les outils utilisés par Statistique Canada pour mesurer le chômage et le sous-emploi. Il vise à comprendre comment ceux-ci influencent la perception des différences entre les hommes et les femmes en matière d’activité et d’emploi. À partir de l’exploitation des données de deux enquêtes canadiennes (l’Enquête sur la population active et l’Enquête sur la dynamique du travail et du revenu), nous analysons les effets genrés de perspective générés par la construction des catégories du chômage et du sous-emploi (inclusives ou exclusives), le choix des périodes temporelles de référence (une semaine ou un mois) et le type d’analyse méthodologique mis en oeuvre (transversal ou longitudinal). Les résultats suggèrent que la façon de traiter les raisons personnelles et familiales conduit à masquer certaines formes féminines de chômage. Il montre plus généralement que les écarts de chômage entre hommes et femmes s’inversent lorsqu’appréhendés à travers des définitions plus inclusives, des périodes de référence plus longues et des analyses longitudinales.
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 | 0.014 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".