Determining a refugee's identity by means of categorical principles : the role of evidence in the refugee determination process : a case study of the Immigration & Refugee Board Documentation Centre
Bibliographic record
Abstract
This project considers the role that evidence plays in determining a refugee's identity within Canada's refugee adjudication process. Its main contention is that Canada's refugee determination system works within a framework that values legalistic and categorical principles, which ignore the complexity of a refugee's identity. Since Canada's refugee system excludes claimants who do not fit designated categories, it encourages them to modify their identity in order to meet the strict criteria for qualification. This project is based on interviews with individuals involved in the refugee process, including refugee decision-maker(s), community activist(s) and refugee lawyer(s). Using important historical and contextual analysis, this paper demonstrates the restrictive nature of refugee definitions and policies that act as barriers that exclude claimants. Moreover, the role of institutions within the Immigration and Refugee Board also operate to restrict claimants. A case study on the IRB Documentation Centre illustrates how evidence, as the determining factor of identity, is one specific method of restricting claimants.
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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 | 0.025 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.042 | 0.021 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| 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".