Bibliographic record
Abstract
For the past decade, Canadian Schools of Social Work have been required to address issues of racism. This ten-year period has been a time of policy and curriculum revamping, yet, the role of the profession itself in sustaining racial hierarchies remains undisturbed. Instead debates have proliferated over the proper language with which to characterize the problem. In these debates, racial difference is most typically described as a "problem" of diversity and oppression. This research analyzes the development of educational policy and how educators understand the process of infusing anti-racist content into the curriculum. Through a Foucauldian discourse analysis of textual data, as well as semi-structured interviews with social work educators, I trace the various linguistic and technical strategies used within the profession to erase race from the educational agenda. I contend that imperial legacies of diversity management continue to suffuse the profession. Recent efforts to conceptualize race must be understood in light of the profession's historical mission to rescue and manage the degenerate. The contemporary social worker appears very much like her imperial counterpart, the bourgeois social investigator. Anti-racist pedagogies, pedagogies built around the idea of self-reflexivity and the examination of white privilege, confront enormous obstacles in a profession that defines its mandate as the practical and benevolent treatment of society's marginalized and "unfortunate" individuals and groups. Pedagogy about race and racism within social work education is structured to fit within the accepted parameters of how practice is defined. Yet the day to day practices on which the profession rests, and which sustain the profession, reproduce whiteness. Thus "doing" race following this same formula functions to reproduce whiteness and race as one more skill at which to be competent. As long as social work practice is synonymous with diversity management and the development of competencies, we remain unable to reconcile being a "good" social worker with anti-racist practice.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.037 | 0.084 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".