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Record W2606350494 · doi:10.1080/10437797.2016.1272516

Intersecting Sexual, Gender, and Professional Identities Among Social Work Students: The Importance of Identity Integration

2017· article· en· W2606350494 on OpenAlexfundaboutno aff
Shelley L. Craig, Gio Iacono, Megan S. Paceley, Michael P. Dentato, Kerrie E. H. Boyle

Bibliographic record

VenueJournal of Social Work Education · 2017
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
FundersRoyal Bank of Canada
KeywordsSexual identityIntersectionalityTransgenderIdentity (music)QueerLesbianSocial workSexual minoritySexual orientationHeterosexismGender studiesPsychologySocial identity theoryHomosexualitySociologySocial psychologyCurriculumProfessional developmentIdentity formationPedagogySelf-conceptHuman sexualitySocial groupPolitical science

Abstract

fetched live from OpenAlex

Discrimination toward lesbian, gay, bisexual, transgender, and queer (LGBTQ) social work students can negatively affect academic performance and personal and professional identity development. Intersectionality is a conceptual approach that states that social identities interact to form different meanings and experiences from those that could be explained by a single identity. This study explored how the educational experiences of LGBTQ social work students in the United States and Canada influenced their professional and personal identities. Using an intersectional analysis, three major themes emerged: the need for social work programs to better promote LGBTQ identity and emerging social work professional identity integration, a lack of LGBTQ content in the curriculum, and unsupportive LGBTQ school climates. Implications for social work education are considered.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.007
Scholarly communication0.0070.003
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.066
GPT teacher head0.464
Teacher spread0.398 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations54
Published2017
Admission routes2
Has abstractyes

Explore more

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