The value of sourcing social work journals for critical discourse analysis
Bibliographic record
Abstract
Using the contents of journals has been an underutilized research approach in social work. Journal archives represent what has been legitimated in the discipline as well as what forms the dominant social work canon. To theorize about journal archival sourcing as a research method, we cite the limited extant examples, drawing out from these the methodology used. We then make a case for the value of journal mining and in particular from the vantage point of critical social work and critical discourse analysis, position the Foucauldian history of the present as an appropriate tool for analysis. We draw this article together by describing how to employ this research method and argue that this might be an exceptionally useful tool at this point of the discipline’s history.
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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 | 0.319 | 0.443 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.033 | 0.029 |
| Science and technology studies | 0.020 | 0.054 |
| Scholarly communication | 0.047 | 0.047 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".