Wooden spoons and “pulling the mom card”: A critical discourse-historical analysis of an interview with a social worker
Bibliographic record
Abstract
Despite increasing acknowledgement within and beyond the social work profession of our role in enacting violence against people we claim to care for and serve, discursive strategies that rationalize and naturalize violence are endemic within social work discourse. In this article, I use critical discourse-historical analysis to examine one interview I conducted with a practicing social worker in Alberta, Canada. Grounded in an intersectional critical approach to inquiry, I elucidate how rationalization strategies for professional violence emerge in ordinary social work talk and situate one social worker’s description of her practice within a historical context of the fabrication of social work in Canada. I focus on this social worker’s reports of threatening clients with a “virtual wooden spoon” to identify linguistic and discursive patterns at play and their semiotic significance as means to rationalize and normalize professional power and control. I specifically situate the analysis within the context of social work as a story of racist, classist, and ableist violence often committed by morally exalted, wealthy, white women. I conclude by reflecting on the importance of examining discursive strategies to rationalize, dismiss or diminish violence in our everyday social work talk.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.050 | 0.068 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| 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".