Bibliographic record
Abstract
This chapter marks the book’s shift from examining the demand for a particular refugee story during the oral hearing, to considering how decision-makers used narratives to test and contest refugee applicants’ testimony. It presents a key finding from the hearings: that decision-makers often engaged in ‘narrative contests’ with the applicant, presenting their own counter-narratives of how events should have taken place if the story presented were to meet the credibility standard of plausibility. The chapter details how the criterion of ‘plausibility’ forges a direct link between credibility assessment and the narrative form, and also sets out the minimal law or policy that governs the testing of oral evidence during the hearing in Australia and Canada. As a result, decision-makers were relatively free to engage in a form of questioning that went beyond asking refugee applicants for information or explanation. Instead, they presented alternative, hypothetical accounts of how events would have taken place if the story (and by implication, the applicant) were credible. When engaging in these narrative contests, decision-makers’ narrative expectations were often deeply subjective, idiosyncratic and unpredictable. The chapter also reveals that in navigating these exchanges, certain applicants displayed high levels of agency and resistance vis-à-vis decision-makers’ own narrative assumptions and their vast power to direct evidence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".