Rethinking Values and Ethics in Social Work edited by RichardHugman and JanCarter. 2016: London, UK, Palgrave Macmillan, 230 pp. ISBN 978‐1‐137‐45502‐4
Bibliographic record
Abstract
Richard Hugman and Jan Carter have published a book that is timely and relevant to, as they say, social work in turbulent times. With 15 chapters from contributors in Australia, Canada, the USA, UK, South Africa and Hong Kong, they have successfully created a text that moves discussions of values and ethics in social work to a new level and depth. The opening preface of the book is chilling in its description of a ‘small, steamy, tropical island in the Pacific, denuded of its natural resources first by colonisation and then by commercial rent seekers, and now hoping to earn its way by hosting refugee detention camps for wealthy Western nations’ (p. vii). The case unfolds to show how social workers are placed in an unenviable position of having to uphold professional values, while being employed by organisations that perpetuate human rights violations. This theme is continued throughout the book in a number of chapters, where the connections between human rights, social justice and social work ethics are debated and delineated. Many cases are used to illustrate the complexities of assessing need, intervening on the basis of a multitude of factors and reflecting on alternate possibilities.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".