Recognizing and Confronting State Subjectivity in Asylum Adjudications
Bibliographic record
Abstract
United States law charges America’s asylum officers with providing humanitarian protection for refugees while simultaneously securing the nation from external threats. This mandate requires that asylum officers balance potentially conflicting claims as they seek to ensure just treatment of claimants. This article explores how officers charged with that responsibility can develop a regime-centred subjectivity that often conditions them to view applicants with fraud and security concerns foremost in mind. This analysis also examines the potential efficacy of practical strategies linked to aesthetic, cognitive, affective, and moral imagination that may allow officials to become more aware of their statecentred subjectivity and how it influences their perceptions of threats to national security and to fraud. This analysis encourages adjudication officers to strive for a more nuanced understanding of what constitutes fraud and national security concerns and what are instead presuppositions created by the United States population-protection agenda.
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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 | 0.003 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".