Bibliographic record
Abstract
The Wrong Mistake: Sending a Refugee HomeThe Federal Court of Canada often imagines refugee law as a special case, a unique and somewhat peculiar domain of legal decision-making.It notes that Refugee Board members are not like other types of civil and administrative decisionmakers, for they must "prognosticate potential risks" in a context fraught by every type of evidentiary complication.1 Theirs is, quite simply, "among the most difficult forms of adjudication." 2 Moreover, refugee claimants are not like other litigants.3 They are a "vulnerable, poor and disadvantaged group" 4 and, for a host of reasons specific to the refugee law context, they are particularly susceptible to having their claims wrongly denied.They stand to pay the price, in other words, for the "radical uncertainty" in this area of fact-finding.5 Imagining claimants as vulnerable people whose pleas for protection may be wrongly denied -who may "cry out for help to no avail" 6 -brings squarely into focus 1 Sivasamboo v Canada (Minister of Citizenship and Immigration) (1994), [1995] 1 FC 741 at para 18, 87 FTR 46, Richard J, citing James C Hathaway, Rebuilding Trust: Report of the Review of Fundamental Justice in Information Gathering and Dissemination at the Immigration and Refugee Board of Canada (Ottawa: Immigration and Refugee Board, 1993) 6-7. 2 Ibid. 3Kabongo v Canada (Minister of Citizenship and Immigration), 2011 FC 1106 at para 31, 397 FTR 191, Martineau J ("Refugee claimants are not ordinary claimants as in civil matters").
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.011 | 0.023 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".