"We Have To Make Sure That We Get It Right": Organizational Impression Management By a Police Service Confronted with Controversy
Bibliographic record
Abstract
This dissertation explores the presentational strategies the Toronto Police Service (TPS) used to respond to three controversies involving its relationship with marginalized and racialized communities - the removal of the TPS from the Toronto Pride parade; the investigation of the serial killer case involving Bruce McArthur; and the Black Lives Matter protests of the summer of 2020. Using an interpretive approach and a variety of conceptual frameworks derived from Goffman’s work on impression management and applying a grounded theory methodology combined with qualitative media analysis (QMA), the dissertation identifies the image management strategies the TPS adopted in each case. The findings show that the strategies were context specific in the sense that they were directed to the precise criticisms being leveled at the TPS in each case. However, there were common themes across the cases which involved acknowledging a problematic past and committing to corrective actions in the future. The main difference in strategies had to do with the degree to which the TPS was prepared to push back on the claims made against the organization and defend its actions. The dissertation speaks to the broader question of how police organizations are attempting to negotiate their legitimacy in a climate where social media has made police-citizen encounters more visible and where recent high profile incidents involving police violence and abuse of power have shaken public confidence and threatened police legitimacy. I argue that taken together, the TPS responses offer a glimpse into how one police organization is seeking to defend its legitimacy by projecting an image of the kind of police service it is aspiring to become, particularly in relation to the marginalized and racialized communities it serves.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.043 |
| Scholarly communication | 0.024 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| 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".