The “Bogus” Refugee: Roma Asylum Claimants and Discourses of Fraud in Canada’s Bill C-31
Bibliographic record
Abstract
The passage of Bill C-31 into Canadian law in June 2012 is part of a discourse created around refugees by the current Government of Canada. Refugees are divided into “good and proper” refugees who live in camps abroad, and the “ fraudulent and bogus” refugees who claim asylum at the Canadian border. The new act, Bill C-31 or Protecting Canada’s Immigration System Act, is analyzed with respect to changes that will result in the systematic exclusion of certain groups of asylum seekers from Canada, based on these discourses of “bogus” and “fraud,” even though these groups may include genuine refugees. Drawing on the case of Czech Roma refugee claimants who come to Canada from Europe, this article shows how the Roma come to stand for the perfect “bogus” refugee — a person who wants to cheat the benevolent Canadian system without having grounds for a successful refugee status application. A critical look at the legislation provides new insights into the relations between governmentality and the regimes of citizenship, with the state performing its power in increasingly spectacular ways. Refugees act as the abject Other that legitimizes, legalizes, and reaffirms such state interventions.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.077 | 0.052 |
| Scholarly communication | 0.020 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.010 |
| 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".