Examining Press Conference and Press Release Accounts of Canadian Police Shootings
Bibliographic record
Abstract
Little research examines the communication work that public police do following police shootings. Based on an analysis of 85 press releases, press conferences, and media interviews after police shootings in Canada spanning 2010–2020, we analyse narrative techniques used in police communications. Contributing to literature on police image management, we examine patterns in these communications, and we also identify silences and absences. We argue police press conferences and press releases after police shootings are less oriented toward misinformation or agenda-setting and more toward risk aversion. Sixty-two percent of communications in our sample used “euphemisms,” which obfuscate elements of use of force, while 31% of communications were “silent” and provided no justification for or information on the shootings. For these reasons, these communications may contribute to a sense of injustice felt by families of the victims of police shootings. Our findings may give pause to police administrators and media liaison officers who should consider what message such risk-averse communications send to families of victims, as well as to the public. In conclusion, we reflect on what these findings mean for literature on police image management.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".