Theorizing as a pedagogical project: how to teach theorizing in social work doctoral education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.015 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it