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Record W4213234707 · doi:10.31235/osf.io/47e3p

Sexual Identity-Behaviour Discordance in Canada

2022· preprint· en· W4213234707 on OpenAlexaboutno aff
Tony Silva, Tina Fetner

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsAmbivalenceIdentity (music)DemographyGovernment (linguistics)Sexual behaviorHomosexualitySexual identityTransgenderPsychologyGender studiesSocial psychologySociologyHuman sexuality

Abstract

fetched live from OpenAlex

This paper uses two nationally representative surveys to examine sexual identity-behaviour discordance in Canada. The first is the Sex in Canada survey (SCS), which is a private survey of 2,303 Canadians. The second is the 2015-2016 Canadian Community Health Survey (CCHS), which is a large government-administered survey with 109,659 respondents. Results from the CCHS show that identity-behaviour discordance and overall rates of same-sex contact are lower in Canada than in the U.S., U.K., or Australia. Still, an estimated 65,700 males and 255,100 females aged 15 to 64 identify as heterosexual yet have had same-sex contact. Age is the only demographic factor which is associated with discordance. Results from the SCS show that about two-thirds of heterosexuals with identity-behaviour discordance are moderately supportive of LGBQ rights and one-third are ambivalent towards them. Future research will need to uncover why a lower proportion of Canadians report same-sex partners and identity-behaviour discordance than their counterparts in the U.S., U.K., or Australia.

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)0.003
metaresearch head score (Gemma)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.314

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.008
Science and technology studies0.0070.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.046
GPT teacher head0.389
Teacher spread0.343 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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