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Record W4306725209 · doi:10.31542/muse.v6i1.2260

Mapping the Police-Media Institutional Relationship

2022· article· en· W4306725209 on OpenAlexaffvenue
Brett McKay

Bibliographic record

VenueMacEwan University Student eJournal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsMacEwan University
Fundersnot available
KeywordsAppealNewspaperConstruct (python library)Cultural criminologyIdeologyPower (physics)Public relationsSociologyPolitical scienceCriminologyLawPolitics

Abstract

fetched live from OpenAlex

The relationship between police and media has been and remains one of the most significant for both institutions. The modern police and modern newspaper developed contemporaneously, each influencing the form, function, and popular appeal of the other. Theories of media and power, however, often address the police as part of larger power structures and ignore the unique police-media institutional relationship.
 This research paper establishes essential characteristics of the police-media relationship and identifies frequent sites of interaction between them, with a focus on crime reporting. Media effects, dominant ideology, and institutional approaches are then assessed as interpretive frameworks, concluding that institutional theory provides the strongest theoretical model for analyzing internal and interorganizational behaviours.
 The professional norms and practices that compose police and media institutional logics are defined, and their historical origins and evolutions are investigated. These long-established logics continue to direct how police and media construct and respond to crime, and consequently how crime is perceived by the public and treated by legal authorities. Because the habits of crime reporting shape policing practices, altering routine media coverage of crime and police issues may help address systemic problems in policing.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.999

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 studies0.0100.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.104
GPT teacher head0.337
Teacher spread0.233 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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