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Record W4391635255 · doi:10.1177/14613557241228075

Policing social media: Are procedural justice principles guiding Canadian police interactions online?

2024· article· en· W4391635255 on OpenAlex
Huda Zaidi, Christopher D. O’Connor

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Police Science & Management · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsSocial mediaProcedural justicePublic relationsPerspective (graphical)Context (archaeology)Criminal justiceSociologyCriminologyNegativity effectPolitical scienceSocial psychologyPsychologyLaw

Abstract

fetched live from OpenAlex

Police presence on social media has become increasingly common in recent years and has arguably altered policing in many ways. Although research in this area is increasing, the growing presence of police on a range of social media platforms requires further examination of the various nuances that continue to emerge regarding this symbiosis. To that end, only a small number of studies have examined this topic from the perspective of police personnel in the Canadian context. Accordingly, drawing on in-depth interviews with police personnel overseeing police social media sites, this article examines how Canadian police services manage negativity and conflict online. The findings suggest that police services address negativity and conflict on their social media sites by drawing on the principles of procedural justice to guide their interactions. We discuss the implications of these findings and how police–public social media interactions might be improved.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.000
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 head0.120
GPT teacher head0.438
Teacher spread0.318 · 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