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Record W4289978733 · doi:10.1177/17488958221114254

Policing and social media: The framing of technological use by Canadian newspapers (2005–2020)

2022· article· en· W4289978733 on OpenAlexaffabout
James P. Walsh, Victoria Baker, Brittany Frade

Bibliographic record

VenueCriminology & Criminal Justice · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsFraming (construction)MainstreamNewspaperSocial mediaPublic opinionPublic relationsLaw enforcementPerceptionPolitical scienceSocial controlMedia relationsDigital mediaNews mediaContent analysisSociologyMedia studiesPoliticsLawEngineeringSocial sciencePsychology

Abstract

fetched live from OpenAlex

As digital platforms that expand opportunities to create, distribute, and access content online, social media are transforming the policing landscape. While scholars have considered social media’s contradictory effects on police services’ public image and operational capacity, less is known about how patterns of technological use are reported within the mainstream press. Employing a mixed-methods content analysis, this article assesses how Canadian newspapers framed the policing-social media relationship over a 15-year period, and how such representations can affect public opinion and policy. It finds, despite minor fluctuations over time and across outlets, news organizations prioritized police perspectives and offered overwhelmingly favourable assessments with social media being constructed as a valuable tool of crime prevention and control. The broader implications of these findings for perceptions of law enforcement and relations between the news media and institutional power are provided.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.116
GPT teacher head0.346
Teacher spread0.230 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

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