Social Work Journals and the Disciplinary Production of Alternative Knowledge(s)
Bibliographic record
Abstract
Disciplinary knowledge is reflected, legitimated, and replicated in academic journals, social work knowledge reproducing mainly Western knowledge(s). Hence, there has been an increase in the calls for a stronger articulation and inclusion of critical alternatives. Using a critical social work lens, we explored whether and how social work journals reproduce alternative knowledges. We developed a novel global list of all 272 social work journals and invited journal editors to respond to a virtual, exploratory qualitative survey. Through our reflexive thematic analysis, we identified two core themes in the 31 responses – the journal editors’ attachment to dominant, white, western social work knowledge and values, and their rhetorical inclusion of alternative knowledges. Alternative knowledges were seldom constructed as subjugated voices, but rather as innovation, gaps, or international perspectives. Despite agreeing that social work journals should include of a variety of knowledges, few journals created intentional space for subordinated knowledges. A disciplining mechanism that excludes/minimizes the alternative voices, and invalidates their experience was used to avoid attention to marginalized and silenced perspectives. Such processes impoverish social work knowledge. To enrich social work knowledge, journal editors should act intentionally, collectively, and critically to identify critical alternative knowledges and facilitate their inclusion in the social work canon.
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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 | 0.044 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.009 | 0.029 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".