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Record W2954762562 · doi:10.3390/socsci8070201

Performing Borders: Queer and Trans Experiences at the Canadian Border

2019· article· en· W2954762562 on OpenAlexafffundabout
Edwin Hodge, Helga Kristín Hallgrímsdóttir, Marianne Much

Bibliographic record

VenueSocial Sciences · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Security and Public Health
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsQueerSecuritizationTransgenderNarrativeBorder crossingBiometricsSpace (punctuation)Border SecurityPolitical scienceGender studiesSociologyLawComputer securityBusinessPoliticsArtComputer science

Abstract

fetched live from OpenAlex

Biometric security and screening systems have revolutionized border crossings. As bodies move across the physical space of the borderland, the border moves through them, scanning and cataloguing and scrutinizing bodies for irregularity. While such technologies have been scrutinized, they have largely been so through heteronormative and cisnormative lenses that fail to recognize the vastly different experiences of nonbinary, nonconforming, transgender, and queer border crossers. This paper examines the implications of what we argue is the individualization of the border, and the effects of biometric security screenings for people whose bodies do not conform to heteronormative and cisnormative standards. We argue that border securitization increasingly equates body differences to narratives of threat and risk, which endangers nonbinary, trans, and queer border crossers, and places their safe passage at risk.

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.319

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0750.027
Scholarly communication0.0110.006
Open science0.0020.008
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0120.001

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 head0.028
GPT teacher head0.368
Teacher spread0.341 · 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 designQualitative
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

Citations4
Published2019
Admission routes3
Has abstractyes

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