“People [Are] Not Dying Because Officers Aren't Following Their Training. ‘It's Because They Are.”: The Construction of Police De-Escalation of Individuals in Mental Health Crises in Canadian Media
Bibliographic record
Abstract
Police are often assumed to be the "de facto" response to people in mental health crises. Contact between police and Canadians with mental illness and in crisis is routine and on the rise (Livingston, 2016). Police responses to these incidences have been criticized after several, highly publicized and tragic encounters between police and people in mental health crisis and the overrepresentation of people of colour in these instances. Media outlets determine which stories are prioritized, how they are framed, and which information to include or omit (Fawzi, 2018). Notably, public knowledge of police activities greatly impacts community perceptions of police legitimacy and social movements with this knowledge largely obtained through media consumption. Little research has focused on how media construct police interactions with people in crisis. Using a social constructionist lens, I conduct a media analysis, including qualitative content (Hsieh & Shannon, 2005), discourse (Berger, 2019), and framing analyses (Wimmer & Dominick, 2014), of Canadian news articles involving interactions between police and individuals in crisis to investigate how the term “de-escalation” is used and framed, which claims are made, who is making these claims, and how mental illness and people of colour are situated in these stories. Police were the most frequent claimsmakers, making 24% of all claims. The dominant narrative of the police is that there is a lack of mental health resources. Alternatively, community claimsmakers suggest the issue is aggressive police responses to people in crisis. Nonetheless, both groups of claimsmakers provide similar solutions, suggesting more mental health resources, greater involvement of mental health professionals in crisis intervention, less involvement of police in crisis intervention, and more mental health and de-escalation training for police officers.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.044 | 0.046 |
| Scholarly communication | 0.023 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".