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Record W2055758934 · doi:10.1080/15504280903472485

Self-Defense, Sexism, and Etiological Beliefs: Predictors of Attitudes toward Gay and Lesbian Adoption

2010· article· en· W2055758934 on OpenAlexaff
B. J. Rye, Glenn J. Meaney

Bibliographic record

VenueJournal of GLBT Family Studies · 2010
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsWilfrid Laurier UniversityUniversity of WaterlooSt. Jerome's University
Fundersnot available
KeywordsLesbianSexual orientationPsychologySocial psychologyHomosexualitySexual minorityDevelopmental psychology

Abstract

fetched live from OpenAlex

While attitudes toward gay and lesbian civil rights issues have generally become more positive, attitudes regarding the rights of gay and lesbian couples to adopt children are relatively more negative. It is proposed that attitudes toward homosexual adoption are rooted in self-protective defense mechanisms, sexism, and beliefs about the etiology of sexual orientation. A sample of Introductory Psychology students was asked to read one of three adoption scenarios. The three scenarios were identical except that the gender composition of the candidate couple was manipulated (i.e., heterosexual couple, gay male couple, or lesbian couple). Results for men and women were analyzed separately because previous evidence suggests that men and women process attitudes related to sexual orientation differently. Men's attitudes toward homosexual adoption were predicted by self-protective defense mechanisms, beliefs about the etiology of sexual orientation, and hostile sexism; women's attitudes were predicted by beliefs about the etiology of sexual orientation, self-protective defense mechanisms, hostile sexism, and benevolent sexism. The importance of a theoretical understanding of the determinants of attitudes toward gay and lesbian adoption and the implications of the current theory for professionals working with gay or lesbian adoption candidates are discussed.

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)0.001
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 score0.015
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.087
GPT teacher head0.397
Teacher spread0.310 · 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

Citations67
Published2010
Admission routes1
Has abstractyes

Explore more

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