Bibliographic record
Abstract
The year 2020 ushered in growing calls to defund the police. In Canada, as in other countries where the movement to defund the police has gained momentum, activists demand transferring money from police departments to social workers, reducing the number of police officers, and increasing police departments’ democratic accountability. This last group of reform initiatives is, perhaps, the least controversial one because it calls for improving the familiar structures of democratic oversight over police departments, such as municipal councils, independent police oversight boards and complaints bodies, and others. The demands for greater accountability of police departments to the public are a symptom of a deeper problem - there is a growing discrepancy between the goals of policing and the consequences of the police’s actions. This discrepancy materializes when the police’s attempts to ensure public safety result in the marginalization of racialized communities, particularly in larger cities across Canada. In order to understand why laudable policy goals lead to deeply problematic consequences, it is necessary to analyze the policing process in our cities. While it has traditionally been assumed that this process is left to the discretion of separate police departments, this paper demonstrates that externalities, such as data generated by private technologies, play an important role in undermining the goals of policing. Reliance on private data and technology does not absolve the police of accountability for resulting human rights violations. However, it has important implications for the reform of public oversight over the police. In an era when non-governmental actors are taking part in law enforcement through procurement contracts, democratic control over the exercise of the police’s contracting powers is an important, albeit often overlooked, instrument of police reform. Relying on contracts for predictive policing technologies as a case study, this paper argues that communities should condition the funding of police procurement on ex ante assessment procedures, technical specifications, and contract enforcement rights. Also, local elected representatives should have an opportunity to approve any data and technology sharing arrangements as well as federal standing offer arrangements that extend predictive policing to their communities.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.006 |
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".