Decriminalizing public space governance: The role of the police
Bibliographic record
Abstract
Punitive criminal justice responses towards essential life-sustaining activities, such as sleeping, bathing and trading in public spaces, have a detrimental impact upon the most vulnerable and marginalized groups in society. These groups include people experiencing homelessness, people who use drugs, migrants, sex workers, LGBTIQ+ persons, persons with disabilities, informal traders, human rights defenders and racial and ethnic minorities. Gender, class and privilege play a key role in enabling and perpetuating these discriminatory processes within the criminal justice system. Laws that criminalize life-sustaining activities, driven by attempts to survive poverty, are often justified on the basis of public health and public order objectives. Unfortunately, law enforcement officials have often been used as a blunt instrument to enforce these laws that target socio-economically vulnerable groups. This approach, of criminalizing poverty and status, has failed to positively address increasing levels of homelessness and poverty while entrenching systemic disadvantage. These laws are found across the Global South in Africa, the Caribbean and South Asia and are frequently based on vague, dehumanizing language while providing law enforcement officials with wide discretion. This article explores strategies to foster non-punitive, human-rights-based approaches to public space governance. It also explores how law enforcement can play a role in preventing crime and violence while enhancing the human capabilities of vulnerable groups in a gender-responsive manner.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".