Disorderly People: Law and the Politics of Exclusion in Ontario
Bibliographic record
Abstract
The Ontario Safe Streets Act is the first modern provincial law to prohibit a wide range of begging and squeegee work in public space. This Act is representative of a much wider set of reforms that the Ontario government has carried out in the administration of criminal justice and social welfare. Central to the neo-conservative character of these reforms has been the construction of “disorderly people,” of those portrayed as “welfare cheats,” “squeegee kids,” “aggressive beggars,” “violent youth” and “coddled prisoners.” Drawing from their expertise in law, sociology, criminology and geography, contributors to this collection make visible the role of law in the practices and logic of a government that polices “public” safety through the exclusion and punishment of some of the most vulnerable people in society. Essays in this collection critique the constitutional soundness of the Safe Streets Act. They document the everyday lives of squeegee workers, map the moral geography of the city, explore the “commodification of crime,” examine the shrinking of both the public and private spaces of the poor, and investigate the “penalty of cruelty” that now characterizes Ontario corrections policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.076 | 0.036 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".