Designated Inhospitality: The Treatment of Asylum Seekers Who Arrive by Boat in Canada and Australia
Bibliographic record
Abstract
This paper argues that there are distinct parallels between changes to the Immigration and Refugee Protection Act enacted by Bill C-31 (2012), in particular the Designated Foreign National regime (DFN), and Australia’s treatment of asylum seekers who arrive by boat. It is contended that recent Australian history and policy demonstrate the perils of adopting an ideology of control and exclusion toward asylum seekers instead of a politics of hospitality, and that Australia’s present political climate provides a stark and salutary warning to Canada, as it follows a similar path of securitization. The paper first explains what is meant by a politics of hospitality. In Part I, it analyzes Australia’s attitude toward, and its treatment of, asylum seekers, focusing in particular on the period since 1989. It is argued that Australia’s inhospitable stance toward asylum seekers has had discernible negative outcomes that provide important lessons for Canada. Part II provides a brief historical overview of Canadian policy toward asylum seekers, followed by an analysis of the DFN regime with reference to international law. It then argues that the DFN provisions contravene the Canadian Charter of Rights and Freedoms. The paper concludes by suggesting that Canada is at risk of following Australia’s security-oriented, inhospitable stance toward asylum seekers.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".