Constructing Crime: Understanding the Roles, Functions and Claims-Making Activities of Media Relations Officers in Ontario
Bibliographic record
Abstract
Despite a growing body of prior research, little attention has been paid to media relations officers (MROs) and how media releases are constructed for the public. This research begins to address this gap by examining the roles and claims-making capacity and activities of police MROs throughout the province of Ontario. Using a sequential qualitative-dominant mixed methods research design, survey data from 19 police services informed the semi-structured interviews conducted with MROs, corporate communication specialists, and civilians (N=26). The findings suggest risk management has a significant influence on how MROs report on crime, inform the public of risk, but also, to educate the public in their role as risk managers. Specifically, crime is constructed so that the likelihood that “something will happen” is emphasized and the public is strongly encouraged to adopt measures to manage their own safety (responsiblization strategies). Thus, I argue that claims-making activities are used by police as a tool of legitimation that is shaped by two dominant discursive frames: (1) As primary definers, constructing crime in terms of risk and promoting citizen risk management; and (2) Projecting positive images of the police to the public. Thus, as legitimation agents, MROs play a key role in justifying and attaining support for the organizational ideals and goals police services value.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".