Transforming field education in social work: A special issue on field education
Bibliographic record
Abstract
Welcome to this special issue of Transformative Social Work, dedicated to social work field education. As we navigate a rapidly changing world, the need for dynamic and impactful field education has never been more crucial. This special issue brings together a diverse range of perspectives and innovative approaches, highlighting how field education can be transformative in both practice and theory. In this collection of thought-provoking articles, we explore the latest research and new practices that are reshaping the way social work students, field educators, and practitioners engage with the field. Articles include discussions on Indigenous field education, developmental and green social work approaches, and field models such as macro placements, self-directed placements, and rotational hospital placements. We also feature the experiences of practicum students during the COVID-19 pandemic and examine what motivates field instructors to engage in field education. As we look to the future, the aspirations and commitments articulated by our contributors in this special issue offer a hopeful vision. The articles in this issue illuminate the adaptability of social work students, field educators, researchers, and practitioners, particularly in the face of unprecedented global disruptions such as the COVID-19 pandemic. The innovations not only address current challenges but also lay the groundwork for more dynamic and responsive field education programs. There is a collective resolve to integrate lessons learned, foster interdisciplinary collaboration, and uphold the principles of social justice that underpin our profession. By doing so, we can ensure that social work field education continues to evolve in ways that are inclusive, effective, and aligned with the needs of our diverse communities. We invite you to explore these contributions, reflect on their insights, and consider how they might inspire and inform your own practice in field education.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.025 | 0.016 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".