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Record W3206874055 · doi:10.3138/cjccj.2021-0021

Examining Press Conference and Press Release Accounts of Canadian Police Shootings

2021· article· en· W3206874055 on OpenAlexaffvenueabout
Kevin Walby, Babatunde Salmon Alabi

Bibliographic record

VenueCanadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénale · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsInjusticeCrisis communicationMisinformationNarrativeCriminologyPolice brutalityPress releasePublic relationsLaw enforcementPolitical scienceSociologyLawAdvertisingBusiness

Abstract

fetched live from OpenAlex

Little research examines the communication work that public police do following police shootings. Based on an analysis of 85 press releases, press conferences, and media interviews after police shootings in Canada spanning 2010–2020, we analyse narrative techniques used in police communications. Contributing to literature on police image management, we examine patterns in these communications, and we also identify silences and absences. We argue police press conferences and press releases after police shootings are less oriented toward misinformation or agenda-setting and more toward risk aversion. Sixty-two percent of communications in our sample used “euphemisms,” which obfuscate elements of use of force, while 31% of communications were “silent” and provided no justification for or information on the shootings. For these reasons, these communications may contribute to a sense of injustice felt by families of the victims of police shootings. Our findings may give pause to police administrators and media liaison officers who should consider what message such risk-averse communications send to families of victims, as well as to the public. In conclusion, we reflect on what these findings mean for literature on police image management.

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)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.268
GPT teacher head0.364
Teacher spread0.096 · 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.

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

Citations6
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénaleSame topicPolicing Practices and PerceptionsFrench-language works237,207