SAFE STREETS FOR REAL PEOPLE: A CASE STUDY OF NEOCONSERVATIVE POLICY FROM A STRUCTURAL SOCIAL WORK PERSPECTIVE
Bibliographic record
Abstract
TheSafe Streets Actof Ontario [SSA] ostensibly regulates aggressive panhandling, but is widely regarded as a contemporary vagrancy law that criminalizes people experiencing homelessness. This paper presents a case study of theSSAin which an ideological analysis is employed to highlight the extent to which dominant political paradigms shape conceptions of social problems and their appropriate remedies. Specifically, it explores the mechanisms inherent to neoconservative ideology which serve to blame individuals for their problems and construct vulnerable people in need of support as villains worthy of exclusion and punishment, rationalizing punitive responses to poverty. This approach is diametrically opposed to the aims of structural social work and therefore must be challenged. An alternative policy response is presented as it might emerge from a social democratic worldview, which is more congruent with social work ideals. This paper thus illustrates how radically the nature of social problems is transformed when viewed through contrasting ideological lenses. The paper concludes that there is great value in using political paradigms to unpack existing and create new policy in the context of structural social work mandates; doing so contributes to the paradigm shift that a profession committed to fundamental social change must help ignite.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.031 | 0.026 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.006 |
| 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".