Media Portrayal of Police Suicides
Bibliographic record
Abstract
Abstract: Suicide is a leading cause of death worldwide but accurate statistics on police suicides in Canada are limited [1], [2]. Statistics from the Ontario Provincial Police show that police suicides may be on the rise with an increase from 1 per year between 1989 to 2012 to 1.7 per year between 2013 to 2019 [3], [4]. How the media portrays police suicide can affect public opinion surrounding the officer and their service and can even affect the suicide rates, known as the Werther effect [5], [6]. This study seeks to examine how police services and specific officers are portrayed in articles concerning police suicides in news media during 2019 in Canada. Data was collected from news media outlets in Canada including The Globe and Mail, CBC News and The Toronto Star among others. Both qualitative and quantitative analyses were conducted on 43 news articles regarding police suicide. Each article was looked at using a three-point system to determine whether the tone was positive, neutral or negative towards the police officer and their service. It is hypothesized that media articles will portray the officers who died in a positive tone but their police service in a negative tone. The findings can be useful as a foundation for future research with the ultimate goal of helping to reduce and prevent police suicides. Implications and direction for future research are also discussed. Keywords: Media, Police, Police Officer, Police Service, Suicide. Title: Media Portrayal of Police Suicides Author: Brandon Berriault, Brandon Frazer, Eleanor Gittens International Journal of Thesis Projects and Dissertations (IJTPD) Vol. 10, Issue 2, April 2022 - June 2022 Page No: 6-15 Research Publish Journals (Publisher) Website: www.researchpublish.com Published Date: 02-May-2022 DOI: https://doi.org/10.5281/zenodo.6511178 Paper Download link (Source): https://www.researchpublish.com/papers/media-portrayal-of-police-suicides
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".