Long walk to safety : experiences of refugee claimants with Canada's refugee policies and practices
Bibliographic record
Abstract
The arrival of refugee claimants in Canada generates interest, controversy and heated debates from the Canadian body politic. Oftentimes, the voices of the refugees are absent from the debates about them and the policies and practices developed to deal with them. Through qualitative interviews, this study examines the experiences of refugees who fled persecution in their countries and came to Canada where they made successful applications for refugee protection. By documenting refugees experiences and their perceptions of these experiences, the study seeks to contribute to the debate on refugees by presenting their perspectives on Canada's inland refugee claim process. The study shows that apart from enduring the pains of persecution from which they fled, refugees also face serious challenges on their journeys to seek refugee protection in Canada. One of the main challenge[s] facing refugees when they arrive in Canada is the complex process of refugee determination and settlement that they have to go through. This paper takes the position that documenting the experiences of successful refugee claimants in Canada can be a good starting point from where to revisit our debates, polices and practices on refugees in a bid to establish a refugee protection system that adequately adheres to national and international refugee legislation while at the same time promoting a more responsive and humane approach to the needs of people fleeing persecution in their respective countries.
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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 | 0.004 | 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 | 0.057 | 0.024 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.009 |
| 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".