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Record W4402729067 · doi:10.1111/josi.12635

Toward inclusive and equitable LGBTIQ+ measurement: Assessing gender and sexual orientation measures and scale validity in national surveys across 21 countries

2024· article· en· W4402729067 on OpenAlexaff
K. Colin Li, Elli van Berlekom, S. Atwood, Yu‐Chi Wang

Bibliographic record

VenueJournal of Social Issues · 2024
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSexual orientationScale (ratio)PsychologySexual behaviorSocial psychologyGeographyCartography

Abstract

fetched live from OpenAlex

Abstract Despite growing global interest in lesbian, gay, bisexual, trans, intersex, and queer/questioning (LGBTIQ+) research, variations in measurement practices across countries have remained underexplored. In this work, we focused on two fundamental aspects of measurement vital to understanding the experiences of LGBTIQ+ people. Specifically, we documented current measures of sex, gender, and sexual orientation used in national mental well‐being‐related surveys and reviewed whether the mental well‐being scales in those surveys have been validated for LGBTIQ+ people. We employed a stratified sampling strategy and evaluated national surveys from a list of randomly selected countries representing 10% of global nations ( N = 21). Fewer than half of the countries measured sexual orientation and fewer than one‐third measured gender beyond the binary in their national surveys. Among the countries that measured gender or sexual orientation, the response options and question phrasing were often not inclusive. In addition, most of the mental well‐being scales lacked validity evidence for LGBTIQ+ populations. Finally, we outline recommendations for the future of reimagining LGBTIQ+ research in terms of measurement, highlighting the importance of research engagement with the global LGBTIQ+ community.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.217
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.199
GPT teacher head0.486
Teacher spread0.287 · 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.

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

Citations11
Published2024
Admission routes1
Has abstractyes

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