Déterminants des heures travaillées au Québec et comparaisons avec l'Ontario
Bibliographic record
Abstract
In this article, we estimate the determinants of hours of work for Ontario and Quebec using Statistics Canada Labour Force Surveys. We first illustrate that intensity hours worked per employed person - has decreased in Quebec relative to Ontario between 1997 and 2005. We then proceed to show that differences in average observed characteristics between the two provinces explain at most 10 % of the difference. We next analyze in more details the impact of industry, occupation, public sector status and union status on the distribution of hours of work. We find that if the size and distribution of unions in Quebec were the same as in Ontario, the proportion of workers in Quebec working reduced hours would decrease significantly while the proportions working long or very long hours would increase. Nous étudions dans cet article les déterminants des heures travaillées par travailleur au Québec et en Ontario à l'aide des Enquêtes sur la population active (EPA) de Statistique Canada. Nous montrons tout d'abord que le différentiel dans l'intensité de la main d'oeuvre a augmenté en défaveur du Québec sur la période 1997-2005. Nous estimons ensuite que les différences dans les caractéristiques moyennes des deux provinces expliquent à peine 10 % du différentiel. Nous analysons finalement de façon plus détaillée l'impact de la structure industrielle, de la structure occupationnelle, de l'appartenance au secteur public et de l'appartenance à un syndicat sur la distribution de heures travaillées. Nous trouvons que ce dernier facteur est le plus important : l'imposition de la structure de syndicalisation ontarienne au Québec diminue de façon significative la proportion de travailleurs à temps réduit et augmente la proportion faisant de longues ou très longues heures.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".