Bibliographic record
Abstract
This paper argues for forming a working group composed of peoples with intersectional, lived experiences of homelessness. The purpose of this group is to consult on implementing the recommendations made to York University Security Services (YSS) by an expert review panel, submitted in December of 2022 in Toronto, Canada. This paper also argues against empowering YSS with the Special Constable provision of the Comprehensive Ontario Police Services Act—a central matter under discussion by the expert review panel. Grounded theory and critical discourse analysis are used in this paper to observe YSS “incident summaries,” published on YSS’s Community Safety webpage, in conjunction with an analysis of the 2022 York University Security Services Review: Final Report. The findings reported in this paper include an approximate 43% overall interaction rate between unhoused people and YSS on the York University campus and a poverty-to-criminalisation pipeline leading to the arrest of unhoused people by Toronto police. These findings give reason to reject empowering YSS with the Special Constable provision. These findings also give reason to consult peoples with intersectional, lived experiences of homelessness on policing and police-proxies, such as YSS.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".