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Record W4224211883 · doi:10.1177/02654075221090678

On guard: Public versus private affection-sharing experiences in same-sex, gender-diverse, and mixed-sex relationships

2022· article· en· W4224211883 on OpenAlexafffund
Karen L. Blair, Odessa McKenna, Diane Holmberg

Bibliographic record

VenueJournal of Social and Personal Relationships · 2022
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsAcadia UniversityUniversity of OttawaTrent University
FundersInstitute of Population and Public HealthSocial Sciences and Humanities Research Council of CanadaNova Scotia Health Research Foundation
KeywordsAffectionPsychologyVigilance (psychology)Social psychologyDevelopmental psychologyCognitive psychology

Abstract

fetched live from OpenAlex

= 96) relationships. Private affection-sharing was similar across groups, except those in mixed-sex relationships reported somewhat less comfort doing so. Despite having a stronger desire to engage in public affection-sharing, those in same-sex and gender-diverse relationships shared public affection less frequently, were less comfortable doing so, refrained from doing so more often, and experienced much higher levels of vigilance related to public affection-sharing, compared to those in mixed-sex relationships. Heightened PDA-related vigilance may have health consequences, as higher levels were associated with worse psychological and physical well-being in all groups. However, individuals in same-sex relationships showed weaker associations between vigilance and well-being than those in mixed-sex relationships, suggesting possible resilience. Still, engaging in vigilance may take its toll, potentially serving as a mechanism through which minority stress works its effects. When we controlled for PDA-related vigilance, psychological and physical well-being levels in same-sex relationships increased relative to mixed-sex peers.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient 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 score0.076
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.225
GPT teacher head0.377
Teacher spread0.152 · 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

Citations15
Published2022
Admission routes2
Has abstractyes

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