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Record W2028223542 · doi:10.1080/00918369.2014.1003014

Measuring Homonegativity: Psychometric Analysis of Herek’s Attitudes Toward Lesbians and Gay Men Scale (ATLG) in Colombia, South America

2015· article· en· W2028223542 on OpenAlexaff
Alexander Moreno, Edwin Herazo, Heidi Celina Oviedo, Adalberto Campo‐Arias

Bibliographic record

VenueJournal of Homosexuality · 2015
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsCentre for Interdisciplinary Research in RehabilitationUniversité de Montréal
Fundersnot available
KeywordsPsychologyScale (ratio)LesbianInternal consistencyContext (archaeology)AnxietySocial psychologyMen who have sex with menHomosexualityConvergent validityClinical psychologyPsychometricsHuman immunodeficiency virus (HIV)PsychiatryMedicine

Abstract

fetched live from OpenAlex

The empirical study of negative attitudes toward gay and lesbian people (homonegativity) is a way to understand the reason for its prevalence. The aim of this study was to examine the psychometric properties of a Spanish version of the Attitudes Toward Lesbians and gay men scale (ATLG). A total of 359 undergraduate students were recruited from two different cities in Colombia, South America. Participants' attitudes toward gays and lesbian people were assessed using the ATLG Scale and the Homophobia Scale; anxiety was measured using a short version of the Zung Self-Rating Anxiety Scale. Internal consistency analyses have shown that the ATLG Scale is a reliable measure of homonegativity in a Colombian sample. In addition, principal components analyses, as well as convergent and divergent validity analyses have confirmed that the ATLG Scale is a valid and reliable measure of homonegativity in the Colombian context and support its use as a research instrument.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.212
GPT teacher head0.417
Teacher spread0.205 · 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.

Study designBench or experimental
DomainMethods
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

Citations28
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of HomosexualitySame topicLGBTQ Health, Identity, and PolicyFrench-language works237,207