Black and Blue: Deconstructing #DefundThePolice
Bibliographic record
Abstract
The demand to address police racism by “defunding the police” echoed on- and offline in the summer of 2020 following the police murder of George Floyd, but it was not always clear what defunding the police entails. Through an analysis of 300 stories posted on the CBC News and CTV News websites between May 25, 2020, and September 30, 2021, this study addresses the construction of the Defund the Police campaign in Canadian online news media and how reflective this construction is of the campaign’s official objectives. Black Lives Matter organized the Defund the Police campaign as a demand for: (1) alternatives to police services; (2) decriminalization; and (3) disarmament, demilitarization and technology. Yet, news media prioritized calls for alternatives to police services, while providing less attention to disarmament, demilitarization and technology demands, and largely excluding decriminalization from defunding conversations altogether. More generally, the news media constructed the Defund the Police campaign around three fluid interpretations: defunding as a call to remove and abolish police, as a call for budgetary reallocation and alternatives to police, and as a call for police reform and accountability. Despite a variety of perspectives, support for a reallocation and alternatives interpretation of defunding was most prominent within the news media, suggesting that police budget cuts in favour of community supports will be the focus of defunding policy in the future.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.027 | 0.075 |
| Scholarly communication | 0.024 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".