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Record W4294577853 · doi:10.1111/bjir.12707

Job‐related well‐being of sexual minorities: Evidence from the British workplace employment relations study

2022· article· en· W4294577853 on OpenAlexaff
Jing Wang, David Wicks, Wenjun Zhang

Bibliographic record

VenueBritish Journal of Industrial Relations · 2022
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsWilfrid Laurier UniversitySaint Mary's UniversityYork University
Fundersnot available
KeywordsLesbianSexual orientationMainstreamSexual minorityPsychologySexual identityScholarshipIdentity (music)Social psychologyHomosexualityAnxietyGender studiesSociologyPolitical scienceHuman sexuality

Abstract

fetched live from OpenAlex

Abstract Despite the increasingly liberal views toward sexual orientation and the evolution of legal rights worldwide, sexual minorities have been an understudied demographic group, especially in mainstream management scholarship. Using a national representative employer and employee linked survey, this study examines the relationship between sexual minority identity and job‐related well‐being. Multi‐level regression analysis reveals that bisexual employees have higher levels of anxiety and depression at work than their heterosexual counterparts. The difference is greater in industries that are not friendly to sexual minorities. When bisexual employees believe their managers are trustworthy and supportive, that difference disappears. No differences are found in well‐being between lesbians, gay men and their heterosexual counterparts. This study provides initial evidence on the effect of sexual minority identity on job‐related well‐being. It also sheds light on the different workplace outcomes between bisexual employees, lesbian women and gay men.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, 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 score0.260
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0080.000

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 head0.075
GPT teacher head0.345
Teacher spread0.270 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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