Theorizing as a pedagogical project: how to teach theorizing in social work doctoral education
Bibliographic record
Abstract
Social work doctoral education is charged with developing stewards of the discipline who can contribute to conserving professional values and generating knowledge to serve marginalized populations and society at large. However, recent studies on doctoral education found that there have been (1) unbalanced curricula between research courses and other courses, such as critical theories and ethics; and (2) a significant gap between research and social justice in doctoral education. Inspired by Swedberg’s work on ‘a process of theorizing’ as a pedagogical project, I have incorporated his ideas into the teaching of a critical theory course in a social work doctoral program. This article aims to articulate how I taught theorizing: I provide an overview of the course, demonstrate how I explain theorizing to students, and outline dialectics of teaching between theories and theorizing to be considered when developing a critical theory course in a doctoral curriculum. I discuss a pedagogical stance and the role of the teacher in theorizing, and suggest practical exercises (such as a ‘theory note’) to assist students in developing their skills in theorizing. Finally, I reflect on lessons learned from teaching this critical theory course, including how I have negotiated with student resistance to theorizing.
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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 | 0.035 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".