Extrajudicial border enforcement against LGBTIQ+ asylum seekers
Bibliographic record
Abstract
Abstract Recent scholarship has highlighted how states differentially restrict the movement of persons who are divergently racialized, gendered, sexualized, abled, and aged. This paper explores the phenomenon of extrajudicial border enforcement—instances where airline officials act on behalf of states to prevent the cross-border movement of marginalized persons. Based on a qualitative analysis of fifty-two cases of failed travel of at-risk lesbian, gay, bisexual, transgender, intersex, and queer (LGBTIQ+) persons attempting to cross an international border between April 2018 and April 2022, this paper offers an intersectional lens into how airline officials, whom the coauthors term extended state agents, deny boarding to individuals through an arbitrary and discretionary process, despite the individual’s compliance with the entry requirements of transit and destination countries. Because state penalties incentivize these boarding denials, implementation of carrier sanctions should be understood as a byproduct of law rather than an exemption from it. These expulsions without due process are in sharp contrast to the LGBTIQ+ rights affirming rhetoric of the expelling states, which we characterize as a form of pinkwashing. In this analysis, coauthors question the responsibility of states in human rights violations consequent to denial of boarding under carrier sanctions regimes.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".