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Record W2971143154 · doi:10.15353/kinema.vi.907

Law and Order and the American Criminal Justice System

2000· article· en· W2971143154 on OpenAlexvenueno aff
David L. Sutton, M. G. Britts, Margaret Landman

Bibliographic record

VenueKinema A Journal for Film and Audiovisual Media · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsLawAmusementCriminal justiceEconomic JusticeOrder (exchange)Criminal lawPolitical scienceWork (physics)Perspective (graphical)SociologyComputer scienceEngineeringBusinessPsychology

Abstract

fetched live from OpenAlex

TELEVISION PROGRAMMES AS LEGAL TEXTS: WHAT LAW AND ORDER TELLS US ABOUT THE AMERICAN CRIMINAL JUSTICE SYSTEM In this work, we take the perspective that although a television program is produced for the brief amusement of a mass audience, it can be viewed as having a part in the scholarly investigation of law and justice in our society. The central question we are focussing on is: What does Law & Order programme tell its audience about the American criminal justice system? According to one of the program's official web sites, Law & Order is a "realistic" television series that examines "law and order from a dual perspective." For roughly the first half-hour, the program focuses on two New York Police Department (NYPD) detectives as they "investigate crimes and apprehend law-breakers." Then the scene switches to the criminal courts, where two assistant district attorneys "work within a complicated justice system...

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.071
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.014
Scholarly communication0.0070.004
Open science0.0000.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.018
GPT teacher head0.317
Teacher spread0.298 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2000
Admission routes1
Has abstractyes

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Same venueKinema A Journal for Film and Audiovisual MediaSame topicLaw in Society and CultureFrench-language works237,207