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Record W2738437060 · doi:10.1080/15332985.2017.1357669

Young bisexual people’s experiences of microaggression: Implications for social work

2017· article· en· W2738437060 on OpenAlexaff
Melissa Marie Legge, Corey E. Flanders, Margaret Robinson

Bibliographic record

VenueSocial Work in Mental Health · 2017
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsDalhousie UniversityMcMaster University
Fundersnot available
KeywordsPsychologyIntrapersonal communicationAmbivalenceGrounded theoryInterpersonal communicationSocial psychologyDevelopmental psychologyGender studiesSociologyQualitative research

Abstract

fetched live from OpenAlex

This article focuses on negative identity experiences reported by young bisexual people in a 28-day daily study, and the implications of these experiences for social work practice. Participants ranged in age from 18–30 years, had Internet access, could read and write in English, and identified as bisexual or felt the label applied to them. Participants who completed at least one diary entry (n = 91) included 49 women, 30 men, and seven genderqueer people. Using a constructivist grounded theory approach, data were organized according to the social ecological model. Erasure, stereotyping, and oppressive discourse microaggressions occurred at both the institutional and interpersonal levels. The majority of reported microaggressions occurred at the interpersonal level, with erasure and stereotyping being most common. Participants also reported three types of intrapersonal microaggressions: internalized oppressions, internalized stereotypes, and ambivalence about coming out. Findings can inform social workers’ efforts to increase their capacity to support bisexual people.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.097
GPT teacher head0.488
Teacher spread0.391 · 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
Published2017
Admission routes1
Has abstractyes

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