The Impact of Liberalization on Female Workers in Quebec: Four Case Studies
Bibliographic record
Abstract
Cet article explore l'impact de la libéralisation des marchés sur la main‐d'œuvre féminine au Québec. Il cherche à valider une intuition formulée par Brunelle, Beaulieu and Minier ( ) en guise de conclusion d'un rapport de recherche mettant en relief l'essor et la prolifération des marchés périphériques du travail dans le capitalisme mondialisé. Parce qu'elles sont surreprésentées dans le travail atypique, les auteurs se demandaient alors si la restructuration des marchés du travail avait des impacts négatifs les femmes. En nous appuyant sur quatre études de cas dans différents secteurs de l'économie (habillement, commerce de détail, télécommunications, services d'aide à domicile), l'article valide l' hypothèse d'une rehiérarchisation genrée du marché du travail sur la base de statuts d'emploi dans le sillage du processus de libéralisation. The article explores the impact of market liberalization on Quebec's female workforce in a context of global capitalism by testing a hypothesis formulated by Brunelle, Beaulieu, and Minier ( ) as a concluding remark of a research that exposed the burgeoning of secondary labor market: “Is liberalization leading to an employment‐statuses‐based restructuration of labor markets that would have negative impacts on women?” Using four case studies in key sectors of the economy (garments, retail, telecom, home‐care services), the article suggests a genderized rehierarchization of labor markets based on employment statuses in the wake of the liberalization process.
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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 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".