Discrimination à l'embauche des immigrants qualifiés: impact des normes formelles et informelles
Bibliographic record
Abstract
Skilled immigrants must pass through a competitive selection process to immigrate to Canada. Despite this, they still have difficulties finding jobs that match their skills. This paradox is mainly due to hiring discrimination. We will first explore the role that anti-discrimination law plays in attempting to resolve this problem. The demonstration of its limits will lead us to look at how hiring discrimination persists. We will argue that the informal Canadian worker norm influences some employers to refuse to hire skilled immigrants. We will finish by arguing that, due to the limitations in formal law and the obstacles created by informal norms, the workplace integration burden needs to shift from the immigrants to the employers through a change in employers' organizational culture. Since employment equity plans have been created to do this, we will study their possibilities and limits. This study will bring to light a number of limitations of formal anti-discrimination laws, concerning the complaints system and the employment equity plans. This thesis highlights structural inequalities in the working world by showing how formal and informal norms impact the main workplace actors, namely employers and potential employees.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".