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Record W2169953519 · doi:10.6000/1929-4409.2014.03.04

Judicial Perceptions of Media Portrayals of Offenders with High Functioning Autistic Spectrum Disorders

2014· article· en· W2169953519 on OpenAlexvenueno aff
Colleen M. Berryessa

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2014
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsnot available
FundersNational Human Genome Research InstituteNational Institutes of Health
KeywordsPerceptionMedia coveragePsychologyCriminal justiceSpeculationCriminologyEconomic JusticeSocial psychologyPolitical scienceLawSociologyMedia studiesBusiness

Abstract

fetched live from OpenAlex

In recent years, sensational media reporting focusing on crimes committed by those diagnosed with or thought to have High Functioning Autistic Spectrum Disorders (hfASDs) has caused societal speculation that there is a link between the disorder and violent criminality. No research exists on how and if the judiciary understands and is affected by this coverage. Therefore this study aims to examine how judges perceive and are influenced by media attention surrounding hfASDs and criminality. Semi-structured interviews were conducted with 21 California Superior Court Judges, including questions on media portrayal. Judges perceived general media portrayals of hfASDs in both positive and negative ways. However, almost all judges who had experienced media coverage surrounding hfASDs and criminality identified it as misleading and harmful to public perceptions of the disorder. These findings suggest judges are not exempt from media attention surrounding violence and hfASDs, and they recognize the potential adverse effects of this negative coverage. Although judges' report their opinions are not affected, the results demonstrate that judges are worried that the public and potentially other criminal justice actors are adversely affected and will continue to be moving forward.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.041
GPT teacher head0.310
Teacher spread0.269 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations24
Published2014
Admission routes1
Has abstractyes

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