Refugee Hotels: The Discourse of Hospitality and the Rise of Immigration Detention in Canada
Bibliographic record
Abstract
While serving as Minister of Citizenship and Immigration Canada between 2008 and 2013, Jason Kenney likened the detention facilities used to house an increasing number of asylum seekers and non-status migrants to hotels. And yet, when addressing the responsibilities of citizenship, he repeatedly argued that “Canada is not a hotel.” However contradictory, Kenney’s references to hotels and, implicitly, to the comforts and privileges they represent, draw on the idea of Canadian hospitality: the suggestion is that we detain asylum seekers in hotels or hotel-like conditions because we are an hospitable people but that our reputation for hospitality leaves us vulnerable to migrants who construe themselves as hotel guests with privileges rather than citizens with responsibilities. Paying particular attention to recent legislative reforms that will almost certainly result in the incarceration of more asylum seekers, this article asks how Canadian hospitality is defined and practiced today. More generally, it uses discourse analysis to explore the tension between the Canadian ideal of hospitality and the realities of an expanding immigration detention system.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.066 | 0.046 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".