RETHINKING REGULATION: INCLUSIONS, EXCLUSIONS AND STRUGGLES
Bibliographic record
Abstract
A recent government report in British Columbia on anti-Indigenous racism in health care calls into question the claim that regulating health care professionals protects the public and ensures a high standard of professional, ethical care. Licensure and regulation have long been debated in social work with strong advocates on each side. The first section of this article revisits the historical and contemporary pro-registration and pro-inclusion arguments. Drawing on publicly available documents central to licensure and regulation in BC, the article then draws on two policy analysis frameworks, namely Indigenous Intersectional-Based Policy Analysis and Bacchi’s framework to explore “what is the problem represented to be” and who is positioned as problematic and erased or delegitimized within these processes. The analysis shows that the regulation debate is a series of practices of power that frame which issues will be “raised and which will not be discussed” such as “harm” and “protection”, while simultaneously eclipsing Indigenous and other non-dominant cultural perspectives and concerns. Our analysis further suggests that mandatory registration constructs the problems facing social workers in depoliticized and narrow ways that do not extend social justice, reconciliation, or decolonization, and require a serious rethink at this moment of change and challenge.
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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 | 0.088 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.020 | 0.124 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 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".