CHAPTER C-4 The Punitive Impact of Physical Distancing Laws on Homeless People
Bibliographic record
Abstract
One of the hallmarks of COVID-19 is that it disproportionately impacts vulnerable individuals and groups.The State's punitive legal responses to the pandemic are no different.This chapter shows why coercive physical distancing laws disparately impact homeless people.It argues that harsh financial penalties for violating these laws can constitute cruel and unusual punishments that contravene s. 12 of the Canadian Charter of Rights and Freedoms.It challenges prevailing s. 12 Charter jurisprudence and demonstrates why expensive fines amount to cruel and unusual punishments even when judges have discretion to modify their severity.After situating the regulation of homelessness within its historical context, it concludes by setting out why homeless people are uniquely vulnerable to over-policing.Ultimately, this chapter elucidates why a public health approach to both COVID-19 and homelessness are necessary and why neither can be punished out of existence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".