MétaCan
Menu
Back to cohort
Record W2254784166 · doi:10.1080/00224499.2015.1124831

Sexual Orientation, Objective Height, and Self-Reported Height

2016· article· en· W2254784166 on OpenAlexafffundabout
Malvina N. Skorska, Anthony F. Bogaert

Bibliographic record

VenueThe Journal of Sex Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsBrock University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSexual orientationTransgenderPsychologyLesbianPsychosocialBiological sexHomosexualitySignificant differenceSame sexDemographyMale HomosexualityGender identityDevelopmental psychologySocial psychologyMen who have sex with menMedicineHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

Studies that have used mostly self-reported height have found that androphilic men and women are shorter than gynephilic men and women, respectively. This study examined whether an objective height difference exists or whether a psychosocial account (e.g., distortion of self-reports) may explain these putative height differences. A total of 863 participants, recruited at a Canadian university, the surrounding region, and through lesbian, gay, bisexual, and transgender (LGBT) events across Canada, self-reported their height and had their height measured. Androphilic men were shorter, on average, than gynephilic men. There was no objective height difference between gynephilic, ambiphilic, and androphilic women. Self-reported height, statistically controlling for objective height, was not related to sexual orientation. These findings are the first to show an objective height difference between androphilic and gynephilic men. Also, the findings suggest that previous studies using self-reported height found part of a true objective height difference between androphilic and gynephilic men. These findings have implications for existing biological theories of men's sexual orientation development.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.045
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.095
GPT teacher head0.415
Teacher spread0.320 · 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 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

Citations14
Published2016
Admission routes3
Has abstractyes

Explore more

Same venueThe Journal of Sex ResearchSame topicGender Roles and Identity StudiesFrench-language works237,207