Transforming Social Work Field Education
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. Transforming 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. Bringing 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. With contributions by : Wasif Ali, Helen Asrate Awoke, Kelemua Zenebe Ayele, Afework Eyasu Aynalem, Nicole Balbuena, Morgan Jean Banister, Natalie Beck Aguilera, Sheila Bell, Heather M. Boynton, Janice Chaplin Mailing, Emmanuel Chinlanga, Jill Ciesielski, Alise de Bie, Emma De Vynck, Cyerra Gage, Anita R. Gooding, Zipporah Greenslade, Annelise Hutchinson, Christine Anne Jenkins, Vibha Kausik, Ermias Kebede, Edward King, Kaltrina Kusari, William Lamar Medley, Karen Lok Yi Wong, Alexandra Katherine Mack, The Ottawa Adult Autism Initiative, Endalkachew Taye Shiferaw, Richardio Diego Suárez Rojas, Margaret Janse van Rensburg, Jennie Vengris, and Courtney Larissa Weaver
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".