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Record W2394698560 · doi:10.1093/bjsw/bcw057

Lesbian, Gay, Bisexual and Trans Health Inequalities: International Perspectives in Social Work, Julie Fish and Kate Karban (eds)

2016· article· en· W2394698560 on OpenAlexaboutno aff
Dr Priscilla Dunk-West

Bibliographic record

VenueThe British Journal of Social Work · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsnot available
Fundersnot available
KeywordsLesbianGender studiesSociologyInequalityEmpowermentWelshSocial inequalityPolitical scienceGeographyLaw

Abstract

fetched live from OpenAlex

Social work has arguably been swept up in more of the ‘psy’ than ‘social’ in recent years, with individualisation and marketisation dominating public life and filtering through to service design and provision. New media are saturated with stories of ‘individual empowerment’ and ‘awareness raising’ as though these were all it takes to shift deeply unequal social relations. This text is a welcome and timely anathema to such discourses so prevalent in contemporary societies. It achieves the broadening of analysis beyond the individual by mapping patterns of inequality in various social and national contexts. Yet this is only the start of this fascinating collection. Part One of the edited collection sets out an ambitious task: to take the reader through Canadian, Italian, Indian and Welsh landscapes with enough depth to connect patterns of inequality in lesbian, gay, bisexual and trans (LGBT) health. This task is achieved and the content is presented in a clear and consistent ‘voice’. The first part of the book is important in establishing the social dimension to health inequalities for LGBT-identified people. Here, legislative frameworks, policies and research are called upon to make the case for patterned inequalities which persist internationally. The complexities related to LGBT experiences are highlighted, with, for example, research highlighting the disparity between so-called ‘progressive’ societies still failing their non-heterosexual citizens.

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
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.651
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 studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.125
GPT teacher head0.395
Teacher spread0.269 · 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 designOther design
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
Published2016
Admission routes1
Has abstractyes

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