Lesbian, Gay, Bisexual and Trans Health Inequalities: International Perspectives in Social Work, Julie Fish and Kate Karban (eds)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".