Police culture, discourse, and the construction of Canadian police officers’ identity
Bibliographic record
Abstract
Canadian police officers work within a deeply rooted and unique police culture that remains anchored to traditional occupational norms and values often resistant to change. Yet, policing is under pressure from elected officials and the public to meet changing social realities and public expectations. Inevitably, officers experience an identity crisis when they feel the strong and persistent pull of their tradition-bound culture while their services attempt to be more inclusive and progressive. Utilizing critical discourse analysis (CDA) and specifically Fairclough’s dialectical-relations approach, this study explores how identity is constructed and reinforced through discourse within and by police culture to create the idea of what it means to be a cop. Using data from an analysis of semi-structured interviews with 30 currently serving police officers in four Canadian police services, it considers how the language of policing (verbal, written, visual) is used to construct police identity. The data show that through the use of police-specific discourse prior to, during, and after recruitment, police culture retains an all-powerful hold on officers’ identity construction before and during recruitment and throughout their careers. It also represents a barrier to more equitable and inclusive police organizations. Finally, this study explores areas such as training, recruitment, and warrior/guardian debate where change should be considered.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.034 | 0.021 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".