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Record W2969968551

Evaluating the Impact of Clinical Evidence about FASD on Attributions and Decisions in a Criminal Justice Context

2019· dissertation· en· W2969968551 on OpenAlexaboutno aff
Katelyn Mullally

Bibliographic record

VenueThe Atrium (University of Guelph) · 2019
Typedissertation
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsnot available
Fundersnot available
KeywordsAttributionCriminal justiceContext (archaeology)PsychologyCriminologyEconomic JusticeSocial psychologyPolitical scienceLawHistory
DOInot available

Abstract

fetched live from OpenAlex

Recent calls for legislative and policy reform to address the overrepresentation of individuals with fetal alcohol spectrum disorder (FASD) in the criminal justice system are likely to increase the amount of FASD-related evidence in Canadian courtrooms. However, the potential impact of these changes for defendants with FASD is unknown. Thus, this study aimed to explore the impact of FASD-related evidence on case judgements in a criminal justice context. Undergraduate (n.=.235) and community participants (n.=.45) read about an adolescent defendant charged with second degree murder, in which the presence of an FASD diagnosis was manipulated; made legal decisions, and rated their perceptions and attributions of the defendant. Participants in the FASD condition rated the defendant as less blameworthy, less responsible, and less able to control his behaviour. Findings suggest that FASD may lead to more lenient case judgements in some cases, though compel additional research in this area.

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)0.025
metaresearch head score (Gemma)0.169
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.169
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.003
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.142
GPT teacher head0.427
Teacher spread0.285 · 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 designObservational
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

Citations1
Published2019
Admission routes1
Has abstractyes

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