Representations of detention and other pains of law enforcement in police museums in Ontario, Canada
Bibliographic record
Abstract
Police museums have been described as dark tourism destinations that depict death and suffering, yet rarely have they been conceptualised as a form of penal tourism. Applying Diarmaid Harkin’s notion of 'pains of policing' to police museums in Ontario, Canada we illustrate how these sites engage in punishment memorialisation. Police museums, which are increasingly staged in the front of police headquarters, are one of the ways policing organisations communicate with the public about their practices, past and present. Engaging with literature on public meanings of police memorialisation, we show how use of force, arrest, interrogation, detention, and other aspects of law enforcement and penality are represented in these museum settings. Drawing from field notes and visual analysis, we argue that these depictions deflect culpability and blame for police violence and abuses, which are normalised in these representations. In conclusion, we reflect on what our findings mean for literature on police museums specifically, and dark and penal tourism sites more broadly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".