Migration inc. : les intermédiaires privés qui font tourner les rouages de la migration de travail temporaire au Québec
Bibliographic record
Abstract
Au cours des dernières années, la croissance du nombre de travailleur·euses migrant·es temporaires au Québec a favorisé le développement d’une infrastructure migratoire commerciale facilitant l’organisation de la migration de travail temporaire. Cette infrastructure se compose de divers intermédiaires privés, tels que les agences de recrutement et les consultants en immigration. Dans cet article, je propose une réflexion sur les rôles et pratiques de ces intermédiaires dans les processus migratoires contemporains. Je décortique leurs multiples fonctions et lieux d’opération et explique comment leurs pratiques contribuent à façonner les précarités et opportunités dans les expériences des personnes migrantes. En insistant sur les champs d’action et les effets des pratiques d’intermédiation, je souhaite mettre en évidence le pouvoir qu’exercent les intermédiaires dans la gouvernance et la gestion de la migration de travail temporaire.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".