We Don't Need To See Them Cry: Eliminating the Subjective Apprehension Element of the Well-Founded Fear Analysis for Child Refugee Applicants
Bibliographic record
Abstract
This article addresses a barrier to effective protection faced by child refugee applicants. Currently all refugee applicants, including infants, are required to satisfy two elements of well-founded fear. All applicants must prove that they face an objective risk of persecution and that they subjectively fear this risk. But children often cannot exhibit the subject apprehension element of the test. As a result, UNHCR, and the U.S and Canadian governments issued guidelines that encourage decision makers to accept other evidence to prove a child's subjective apprehension when the child is unable to exhibit fear. However, this approach does not go far enough. By allowing subjective apprehension to remain a part of the well-founded fear analysis for child refugee applicants, the threat of effective protection being denied is quite real. A recent case in the United States highlights this point. A nine-year old hearing impaired child was denied asylum by an immigration judge despite objectively clear evidence of potential risk of persecution simply because the child did not satisfy the subjective apprehension requirement. In order to protect child refugee applicants from such mis-guided decisions in the future, this article proposes a solution of only requiring objective risk evidence from child refugee applicants in order to establish a well-founded fear.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".