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Record W3048724337 · doi:10.1177/0964663920946360

Do the Challenges of LGBTQ Asylum Applicants Under Dublin Register With the European Court of Human Rights?

2020· article· en· W3048724337 on OpenAlexaff
Raoul Wieland, Edward J. Alessi

Bibliographic record

VenueSocial & Legal Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsMcGill University
Fundersnot available
KeywordsHuman rightsTransgenderRefugeeQueerVulnerability (computing)LesbianHarmPolitical scienceInvisibilitySociologyLawCriminologyGender studies

Abstract

fetched live from OpenAlex

Evidence suggests that Europe’s Dublin Regulation is increasing the precarity of lesbian, gay, bisexual, transgender, and queer (LGBTQ) asylum applicants. Dublin allocates responsibility for examining asylum claims between EU Member States. The European Court of Human Rights (ECtHR) guides the obligations of States under Dublin. Increasingly, the ECtHR draws on the concept of vulnerability to frame the experiences of asylum seekers. Vulnerability purportedly functions for the ECtHR as a lens through which the harm experienced by asylum applicants is magnified, enabling it to better recognize human rights violations. Nevertheless, the ECtHR’s vulnerability lens may be distorted by hetero- and cisgender normativity. We explore some implications of the ECtHR’s assumptions for how the vulnerabilities of LGBTQ asylum seekers in Europe under Dublin register with the ECtHR. We suggest that the combined frameworks of intersectional invisibility and layers of vulnerability can improve the ECtHR’s capacity to understand how LGBTQ asylum applicants may be particularly vulnerable under Dublin.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0110.025
Scholarly communication0.0120.011
Open science0.0020.016
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.083
GPT teacher head0.348
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2020
Admission routes1
Has abstractyes

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