Neoliberal Deviants and Surveillance: Welfare Recipients under the watchful eye of Ontario Works
Bibliographic record
Abstract
This article examines the current practices of welfare surveillance in Ontario Works (OW). Although neoliberal policy changes to social assistance have been well documented by a variety of scholars, the surveillance technologies behind them have received less scrutiny. The article questions how new surveillance technologies have transformed the administration and everyday practices of OW. Based on primary research of policy documents, legislation, regulations and directives, the paper explores the eight surveillance tools used to police OW recipients including the Consolidated Verification Procedure (CVP); Maintenance Enforcement with Computer Assistance (MECA); Service Delivery Model Technology (SDMT); Ontario Works Eligibility Criteria; Eligibility Review Officers (EROs); Audit of Recipients; Drug Testing and Welfare Fraud Hotlines. I argue the Ontario Works Act (OWA) 1997 justified increased surveillance, regulation and control of poor families creating new forms of surveillance. Additionally, the rationales behind the implementation of OW surveillance (anti–fraud and workfare) were unjustified and have made OW recipients, particularly racialized single mothers more vulnerable. Using a feminist political economy critique, the article endeavours to explore the gendered, classed and racialized implications of welfare surveillance and the expanding ways the state has created ‘deviants’ out of those who fail to be ‘good market citizens’.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.024 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".