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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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.010
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.232
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 teacher head, not a consensus.

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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