What does hospitality look like when immigrants are ‘wanted’? The case of the immigration selection process in Quebec, Canada
Bibliographic record
Abstract
Recent scholarly work in the social sciences has engaged with the concept of hospitality in order to explore immigration dynamics, especially in relation to the situation of asylum-seekers. As an ambivalent concept, it captures the tension between, on the one hand, the act of hosting and welcoming foreigners and, on the other, controlling their entry. In this article, I reflect on the relevance of this concept for the study of the bureaucratic process of selecting qualified immigrants in Quebec, which aims to identify those future ‘skilled’ immigrants who would be most likely to integrate, both culturally and economically. These migrants, far from being unwanted, constitute the core of the federal and provincial immigration policies and the main mechanism through which a foreigner could obtain permanent residency in Canada. Through a fictional narrative based on my fieldwork with permanent residents living in Montreal, I show how the immigration selection system works in practice. Applied to the case of selected immigrants, the concept of hospitality forces us to distinguish practices from national discourses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.051 | 0.018 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".