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Record W4226315454 · doi:10.1177/17416590221086543

Regimes of representation in Canadian police museums: Othering, police subjectivities, and gunscapes

2022· article· en· W4226315454 on OpenAlexaffabout
Haley Pauls, Kevin Walby, Justin Piché

Bibliographic record

VenueCrime Media Culture An International Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsUniversity of OttawaUniversity of Winnipeg
Fundersnot available
KeywordsCriminalizationRepresentation (politics)HarmNarrativeSociologyCriminologyIdeologyLaw enforcementPolitical scienceMythologyMedia studiesLawPoliticsArt

Abstract

fetched live from OpenAlex

There are dozens of public police museums located across Canada that memorialize the country’s history of law enforcement and criminalization. Drawing from fieldwork at these sites, we explore the representational devices used to curate police museum displays. Invoking Stuart Hall’s work on representation and Othering, we examine how gun displays at Canadian police museums are organized to minimize the harm that police interventions with guns cause. Arguing these displays are made intelligible through a regime of representation that naturalizes the distinction between police officers and the “criminal” Other, we examine how these museums position weaponry including the gun as an esthetic object and a force of social good when in the hands of police. Analyzing curatorial strategies such as the arrangement of weapons, mannequin placement, dress, and level of humanization, as well as the rhetoric and narratives espoused on accompanying placards, we show how the curatorial approach in these spaces ratify an ideological framework that normalizes police violence and criminalization. We then assess what our analysis contributes to literatures on police museums and policing myths.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.805

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.048
GPT teacher head0.383
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2022
Admission routes2
Has abstractyes

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