Deconstructing Gender in Asylum Categories: An Archival Perspective on a Practice with Limited Access
Bibliographic record
Abstract
Public authorities make decisions that greatly impact both citizens and non-citizens. Decision-making on asylum, which is regulated by international law but administered by states, in particular is characterised by a higher level of secrecy than other public services. The 1951 Refugee Convention defnes refugeehood as the fear of being persecuted for reasons of race, religion, nationality, social group, or political opinion. Although fear of gender-related persecution was not included as one of the grounds meriting asylum, state practice means that it is today generally recognised as such. The United Nations Refugee Agency (UNHCR) recommends that states "ensure a gender-sensitive interpretation of the 1951 Refugee Convention." Using natural language processing (NLP) to analyse an open dataset of Danish asylum case summaries, we frst identify fve empirical categories connected to gender in the case summaries: 1) gender-related persecution, 2) LGBT 3) sexual conditions, 4) marital conditions and 5) other gender-related forms of persecution. Secondly, we illustrate the relationship between these gender-related categories and other categories/topics in asylum motives. Finally, we discuss how data science techniques can be applied to better understand complex, cooperative work practices in an area where access for researchers is limited, but archival data is available.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.087 | 0.001 |
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; both teacher heads agree on what is shown here.
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".