UNSETTLING CONCEPTIONS OF POWER THROUGH TEACHING AND LEARNING CRITICAL REFLECTION ON SOCIAL WORK PRACTICE
Bibliographic record
Abstract
Social work education is expected to offer students the opportunity to develop the skills necessary for critical self-reflection as it relates to professional practice. In this paper, we will describe how a model of critical reflection is taught and practiced within our MSW program in a Canadian School of Social Work. As a professor and student within the course, we describe our experience of engaging with the incident that the student used to learn the underlying theories and process of critical reflection. Her experience involved recognizing previously taken-for-granted conceptions of power, which she explored in her final paper for the course. We continued to critically reflect together following completion of the course, and our explorations are presented and expanded upon in this paper as an example of the potential of critical reflection, and as a reminder of the importance to continually reflect upon the complexity of power. Although we began with differing conceptions of power, we agree that power is neither solely ‘bad’ nor ‘good,’ but rather is complex, fluid, and relational. The paper provides an example of the benefits of incorporating opportunities for sustained critical reflection in social work education and concludes with implications for social work practice.
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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 | 0.054 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.010 | 0.108 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".