Policing the Pandemic: Tracking the Policing of Covid-19 across Canada
Bibliographic record
Abstract
The Policing the Pandemic Mapping Project was launchedon 4 April, 2020 to track and visualize these massive andextraordinary expansions of police power and the unequalpatterns of enforcement they are likely to produce. In doingso, we hope that we can bring to light patterns of policeintervention, to help understand who is being targeted, whatjustifications are being used by police, and how marginalizedpeople are being impacted. More broadly, we hope theproject will inform a larger conversation about the role ofpolicing in society, to scrutinize public health and policecollaboration, and to focus attention toward the harms ofcriminalization. Having an understanding of these patterns incoming weeks will help inform approaches to actively resistthe logic and practices of policing crisis and disease, ratherthan allow them to become widespread and normalized.Through the acts of identifying, reporting, and visualizingevents related to the policing of COVID-19, the projectoffers a living repository of publicly accessible data that canbe used by activists, academics, journalists, and communitymembers to analyze, discuss, and challenge the policing ofdisease. We encourage all people to use the data availablethrough this project in any way they wish.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".