Policing the Pandemic: Tracking the Policing of Covid-19 across Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it