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Record W2948621935 · doi:10.22329/csw.v9i1.5758

Demarcating Gender and Sexual Diversity on the Structural Landscape of Social Work

2019· article· en· W2948621935 on OpenAlexaffvenue
Nick J. Mulé

Bibliographic record

VenueCritical Social Work · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsYork University
Fundersnot available
KeywordsGender studiesSociologyOppressionIntersectionalityNormativeInclusion (mineral)EmancipationSocial changeSocial psychologyPolitical sciencePoliticsPsychology

Abstract

fetched live from OpenAlex

The importance of demarcating gender and sexually diverse populations in structural social work theory is discussed from a differently centred cultural group perspective highlighting distinct qualities that fall outside normative gender identities and heterosexuality. Historical oppression experienced by these populations has likened their inclusion in structural social work theory yet the continued marginalization of these populations and associated implications are not to be lost sight of. A means of bringing currency to structural social work theory with regard to these populations is to embrace liberationist goals taking intersectionality into consideration. Such goals are in alliance with the social work values of acceptance, self-determination and respect working towards social justice and emancipation, and go far beyond the rights-claims equality agenda that sustains a slightly varied hegemony, giving the social location of gender and sexually diverse groups relevancy and viability on the structural landscape of social work.

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)0.010
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.002
Science and technology studies0.0110.110
Scholarly communication0.0130.012
Open science0.0010.016
Research integrity0.0030.004
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 head0.061
GPT teacher head0.366
Teacher spread0.304 · 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

Citations16
Published2019
Admission routes2
Has abstractyes

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