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Record W4378905157 · doi:10.1215/00703370-10769825

Sexual Orientation Identity Mobility in the United Kingdom: A Research Note

2023· article· en· W4378905157 on OpenAlexaff
Yang Hu, Nicole Denier

Bibliographic record

VenueDemography · 2023
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of Alberta
FundersEconomic and Social Research CouncilUniversity of Essex
KeywordsSexual identitySexual orientationEthnic groupIdentity (music)PopulationSexual minorityPsychologySocial psychologyGender studiesDemographyDevelopmental psychologyHuman sexualitySociology

Abstract

fetched live from OpenAlex

Sexual identity is fluid. But just how fluid is it? How does such fluidity vary across demographic groups? How do mainstream measures fare in capturing the fluidity? In analyzing data from the United Kingdom Household Longitudinal Study (N = 22,673 individuals, each observed twice), this research note provides new, population-wide evidence of sexual identity mobility-change and continuity in individuals' sexual orientation identification-in the United Kingdom. Overall, 6.6% of the respondents changed their sexual identity reports between 2011-2013 and 2017-2019. Sexual identity mobility follows a convex pattern over the life course, with higher mobility rates at the two ends than in the middle of the age spectrum. Sexual identity mobility is more prevalent among women, ethnic minority individuals, and the less educated. Changes in people's self-reported sexual identity are closely associated with changes in their partnership status and partner's sex. However, inferring individuals' sexual identity from their partner's sex substantially underestimates the degree of sexual fluidity compared with people's self-reported sexual identity. Our findings encourage researchers and data collectors to fully examine sexual identity mobility and consider its implications for measuring sexual identity.

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 score0.368
Threshold uncertainty score0.732

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.232
GPT teacher head0.523
Teacher spread0.292 · 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

Citations25
Published2023
Admission routes1
Has abstractyes

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