Book Review - The Social Work, Cats and Rocket Science Guide to Rights-Based Practice: An A-Z, from Advocacy to Zones of Influence, Elaine James and Rob Mitchell (eds)
Bibliographic record
Abstract
Elaine James’ latest work in collaboration with Rob Mitchell is an introductory text for new social work students, which unfortunately does not actually have anything to do with cats or with rocket science. It uses a unique A to Z framework to introduce aspiring social workers to the various aspects of working with adults in the UK, including relevant policies, social work values, and practice challenges. Policies like the Care Act 2014 are related in simple terms and their direct applicability to social work practice is explained with clear examples. How social work values and standards are applied to practice with adults, such as diversity, empowerment, therapeutic relationships, and advocacy are presented in conjunction with practice examples from the authors’ experiences. The use of experiences from various areas of social work (for example, mental health, adults with disabilities, substance use issues, and the criminal justice system) increases the accessibility of the text and makes it ideal for use with first year social work students. The centrality of advocacy as a key social work requirement is excellently done and highlights to social work practitioners the need to look beyond the immediate case before them and consider the wider challenges faced by their clients and use their position to support the voices and needs of their clients within various institutions.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.049 |
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".