Le degré de déqualification professionnelle et son effet sur les revenus d'emploi des femmes immigrantes membres d'une minorité visible du Québec
Bibliographic record
Abstract
In recent decades, the increased participation of women in the labour market and the rise in the number of landed immigrants in Canada have largely contributed to transforming the employment market. However, several studies have shown that women, immigrants, and members of visible minority groups continue to make up the most vulnerable segments of the job market. The issue we want to address in this article speaks particularly to outdated skills, which means working in a job that requires an academic level below the one held by that individual. In fact, an individual who is too qualified for a position is not fully using his or her human capital, which constitutes a loss for the individual as well as for society. The author's objective is to verify if the skills of immigrant women who are members of a visible minority in Québec are more at risk of becoming outdated than those of men and women from other groups, resulting in a cross-over between immigrant status and belonging to a visible minority. She also wants to analyze the impact of outdated skills on job income in these different groups in order to identify which group is most penalized by outdated skills in the matter of employment income. Her results, based on the 2006 Census files, show that immigrant women who are members of a visible minority in Québec are indeed the most affected by outdated skills and have, as a general rule, the lowest average weekly employment income of all groups. However, this group does not incur the highest income loss associated with outdated skills.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".