Can historians capture refugees’ voices from the records of their applications for asylum?
Bibliographic record
Abstract
This article examines how evidence given in refugee appeals tribunals is processed and transformed from an oral to a written form and then summarised and analysed for legal purposes. Its sources are the determinations of tribunals in New Zealand, Australia, Britain and Ireland which cite what refugees have said when they provide the reasoning for their decisions. This takes a summarised form, intended as an evidentiary basis for a legal decision, not to investigate the personal histories of the refugees themselves. Yet the enormous volume of this material available from the tribunal archives in Australia, New Zealand, Britain, Canada and some other European countries is a valuable historical source, with severe limitations on how it should be understood as a record of oral testimony. It is limited by the anonymising of the records, to a greater or lesser degree, and the by nature of the relationship between the refugees and the bureaucracy that collects their statements, but where the voice of refugees breaks through the legal editing of their testimony, it has a powerful resonance. It is important because it is part of the refugees’ efforts to make their stories heard.
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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 | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".