Resisting Dogmatism in Social Work Knowledge Generation: Theorising, Social Justice and Implications for Social Work Doctoral Education
Bibliographic record
Abstract
Abstract Social work doctoral education is charged with the task of generating and critically evaluating knowledge to inform and transform professional practice. To promote the foundational values of social justice and diversity in social work, scholars highlight the importance of multiple ways of knowing and multiple ontological perspectives in social work knowledge generation. Yet, critical scholars have raised concerns about a reviving dogmatism in the philosophical and theoretical orientations (e.g. positivism and empiricism) of social work knowledge. Recent studies also show a significant gap between research and social justice in the social work doctoral curriculum. Critically reflecting and problematising this ‘social’ phenomenon, I argue that it is essential to engage in ongoing theorising to resist dominant discourses, represent marginalised voices in social work knowledge and furthermore, to teach students how to theorise in doctoral education. Drawing from Foucauldian theories on knowledge and power, this article first contextualises the dangers of dogmatism in knowledge generation in social work. Then, I provide a brief review of the historical development of theorising, introducing Richard Swedberg’s work on the topic, particularly his four steps in the ‘process of theorising’. How this noble sociological ‘theory of theorising’ can be incorporated into social work is discussed.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.061 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".