Lives in the Balance: Understanding current challenges to the refugee claim process
Bibliographic record
Abstract
Canada’s refugee determination system has been the subject of recent \npublic commentary, much of it focusing on “abuse” and alleged \nproblems in the system. \nThe realities of refugees in the system have received less attention. \nRefugee determination is complex and challenging. It is not easy to \ndecide who needs protection and who does not. \nFor refugees, however, there is a simple but compelling need for \nprotection from persecution. For Canadians too, there is at heart a \nsimple challenge: will we ensure that refugees are not sent back to face \npersecution? \nWhat follows are some answers to current questions and concerns \nabout the system, as well as information about a few of the individuals \nin the system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.042 | 0.055 |
| Scholarly communication | 0.033 | 0.030 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".