Transforming Social Work Field Education: New Insights from Practice Research and Scholarship
Bibliographic record
Abstract
Social work field education in Canada is in crisis. New understanding and approaches are urgently needed. Innovative and sustainable models need to be explored and adopted. As professionals, social workers are expected to use research to inform their practice and to contribute to the production of research. Yet many social workers are reluctant to integrate research into their practice and into field education. \n \nTransforming Social Work Field Education encourages the adoption of research and scholarship into the practice of social work, especially field education. It offers current theoretical concepts and perspectives that shape social work field education and provides case studies of practice research grounded in the experiences of diverse communities and countries. Highlighting cutting-edge research and scholarship, each chapter addresses critical issues in social work practice and their implications for field education. \n \nBringing together scholars at various stages of their careers, this book fosters a meaningful dialogue on the dynamic, complex, and multi-faceted nature of social work practice, research, and innovation in the critical area of field education. A vivid and original work, it stimulates interest and discussion on the integration of research and scholarship in social work field education in Canada and around the world.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.026 | 0.059 |
| Scholarly communication | 0.026 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".