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Record W4232811659 · doi:10.32920/ryerson.14644839

Exploring the use of systematic and heuristic processing in the courtroom: the effect of evidence modality on jurors’ decision-making processes

2021· preprint· en· W4232811659 on OpenAlex
S. J. Freedman

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicJury Decision Making Processes
Canadian institutionsSystems, Applications & Products in Data Processing (Canada)Bishop's University
Fundersnot available
KeywordsVerdictDeliberationPsychologyJurySocial psychologyTask (project management)LawPolitical science

Abstract

fetched live from OpenAlex

The use of technology in the courtroom is increasingly commonplace. While some research has explored how technology may influence jurors throughout the trial itself, there has been little focus on how it might influence jurors during the deliberation period, or whether it affects their verdicts. The current study assessed whether the form of evidence available during the decision-making period, along with the mock juror’s level of motivation for the task, affects how trial information is processed and how verdict decisions are made. Mock-jurors (N = 243), half of whom were explicitly informed of the task’s importance, watched a video of a murder trial. During the decision-making phase, some jurors were then given the opportunity to review the trial video, transcript, or both before rendering a final verdict. While there were no differences in verdicts as a function of review condition, the amount of content mock-jurors reviewed differed by review condition.

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.

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)0.011
metaresearch head score (Gemma)0.088
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.088
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.493
GPT teacher head0.445
Teacher spread0.048 · 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

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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