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Record W2053018917 · doi:10.1375/acri.43.3.444

‘Cop[ying] it Sweet’: Police Media Units and the Making of News

2010· article· en· W2053018917 on OpenAlexaboutno aff
Alyce McGovern, Murray Lee

Bibliographic record

VenueAustralian & New Zealand Journal of Criminology · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsnot available
FundersCharles Sturt UniversityUniversity of Western SydneyUniverzita Karlova v Praze
KeywordsNewspaperSoftware deploymentArgument (complex analysis)Political scienceUnit (ring theory)Media coverageQuarter (Canadian coin)Public relationsAdvertisingMedia studiesSociologyGeographyCriminologyEngineeringLawBusinessPsychology

Abstract

fetched live from OpenAlex

Over the past two decades police media units have played an ever-increasing role in managing the dissemination of information between the police and media organisations. Using the example of the New South Wales Police Media Unit in Australia (hereafter NSW PMU) this article assesses the journalistic deployment of PMU information and develops a broader sociopolitical argument explaining the growth of PMUs more generally. We analyse qualitative research data, in the form of interviews with journalists and NSW PMU staff (n = 29), and quantitative data from an analysis of two Sydney-based daily newspapers. We suggest that the growth of PMUs can be explained with reference to new programs of governing crime that developed throughout the last quarter of the 20th century as well as significant changes to the global media landscape.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0120.019
Scholarly communication0.0140.012
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.172
GPT teacher head0.401
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), 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

Citations62
Published2010
Admission routes1
Has abstractyes

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