Transforming Social Work Field Education, New Insights from Practice, Research and Scholarship, Edited by Julie L. Drolet, Grant Charles, Sheri M. McConnell, and Marion Bogo
Bibliographic record
Abstract
This book provides a collection of voices from people involved in practice education, varying from social work practitioners and students to people with lived experience and placement providers. The result of this collective approach is a diverse and rich account of issues impacting practice education and, given some of the presenting challenges, a rights-driven way forward. The whole book is underpinned by a commitment to social work values and individuals rights; from the individual right to experience inclusive services to the rights of learners to access education in a variety of ways, with common agreement that the ‘field placement’ is an essential element of all programmes. The term ‘field placement’ is used in this book, but this be applied to all settings where there is an experiential element to the social work education context. There is a potentially wide-reaching audience for this book, from those providing social work education in formal establishments to social work practitioners supervising students on placement and, importantly, the services that provide students with rich learning opportunities as well as students themselves.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".