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Record W4386252649 · doi:10.1177/07340168231196995

“It's Just Like on TV”: An Analysis of the Mirandizing Process on TV

2023· article· en· W4386252649 on OpenAlexaff
Dakota Wing

Bibliographic record

VenueCriminal Justice Review · 2023
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsYork University
Fundersnot available
KeywordsSuspectRepresentation (politics)InvocationOrder (exchange)Due Process ClauseLawPsychologyProcess (computing)Political scienceCriminologySocial psychologyComputer scienceSupreme courtBusiness

Abstract

fetched live from OpenAlex

When Mirandizing a suspect, officers sometimes compare the Mirandizing process to their representation on TV. In doing so, officers assume the suspect (and more generally the American public) is familiar with, and understands, their Miranda warnings due to their dissemination on TV. Thus, this paper investigates how the Mirandizing process is presented on TV. An analysis of arrests and custodial interviews on Law & Order: SVU indicates that fictional suspects are rarely adequately Mirandized; they are either not Mirandized at all or are provided a partial version. Moreover, suspects on TV are found to attempt to explicitly invoke their rights only 11% of the time, of which there is about a 50–50 chance of the attempted invocation being successful. In 23% of the time, legal representation appears without any language from the suspect showing them invoking their rights. Attempted and implied invocations on TV are primarily made by persons guilty of the crime they are being accused of, and innocent suspects primarily waive their rights, reinforcing a popular belief that guilty people invoke their rights and innocent people waive them.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.178
GPT teacher head0.461
Teacher spread0.283 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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