LGBTQ People and Social Work: Intersectional Perspectives, Brian J. O'Neill, Tracey A. Swan and Nick J. Mule (eds)
Bibliographic record
Abstract
The Canada-based editors of this book introduce it as having evolved over many years. That it has a rich history is evident from the briefest glance at the contents page and confirmed by the many excellent essays it contains. The book is ambitious in its aims. It seeks to recognise the changing environment and experiences of lesbian, gay, bisexual, transgendered, queer (LGBTQ) people and the context of social work approach to practice with marginalised groups. It forefronts and honours the experiences of LGBTQ people whilst at the same time being explicit about its transformative agenda. Importantly, the book focuses on the notion of intersectionality whilst avoiding simple definitions and giving free rein to the contributors to create connections, identities and subjectivities. The result is a book which reflects the challenges of both living and working in a fluid, political and disputed terrain. The book is presented in three sections across eighteen chapters. Interestingly, the book begins with some of the most challenging reading. Part 1 focuses upon political and personal and tensions within and across LGBTQ communities—uncomfortable but important reading for those of us who identify as LGBTQ. These chapters allow insight into the complex diversity of identity whilst highlighting the narrowed reflection of understanding of LGBTQ diversity in social work service delivery. Additionally, authors discuss the mechanisms and methods through which members of LGBTQ communities reflect and uphold oppression and marginalisation of LGBTQ people who have less power. Aging, disability and bisexuality are each thoughtfully discussed but the standout chapter in this section is the reflective, conversational account of two black social workers who work with ‘Queer Black Youth’ (author's label). This chapter considers the interconnectedness of social work skills, personal identity and professional identity and their attempts to unravel the dynamic without using whiteness as a baseline for understanding.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.028 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".