Critical Spaces in the Canadian Refugee Determination System: 1989-2002
Bibliographic record
Abstract
This paper draws its conclusions from a multidisciplinary study of the refugee determination process in Canada, the aim of which was to examine the strengths and weaknesses of the system and to explore means of improving it through an in-depth analysis of the diversity of attitudes and perceptions of different actors involved in the process. The basic hypothesis is that the legitimacy of the action of the Immigration and Refugee Board (hereafter IRB) is challenged because of a series of disagreements on the way it operates. Using interviews with former Board members, as well as with other professional actors of the system (lawyers, NGO workers, interpreters, health professionals), we try to understand better the parameters of the problems facing the IRB on three sets of issues: the appointment and renewal of Board members; the relationships between Board members within the IRB; the evaluation of the evidence by Board members. All issues relate mainly to the principles of independence and impartiality of the IRB, as an expert administrative tribunal. In particular, using the idea of ‘critical space’ as a conceptual framework, this study tries to ascertain more precisely how critical spaces within the IRB were being used in order to foster a common culture of independence and impartiality within the institution, or not. This study covers the period 1989-2002: it signals reforms accomplished since and suggests more means for improvement.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.054 | 0.023 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.004 | 0.011 |
| 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".