MétaCan
Menu
Back to cohort
Record W7133044954

The challenge for cause: does it reduce bias in the jury system?

2004· dissertation· W7133044954 on OpenAlexaboutno aff
Dax Urbszat

Bibliographic record

VenueTSpace · 2004
Typedissertation
Language
FieldSocial Sciences
TopicJury Decision Making Processes
Canadian institutionsnot available
Fundersnot available
KeywordsJuryJury selectionHung juryTest (biology)Economic JusticePrejudice (legal term)CausationJury instructions
DOInot available

Abstract

fetched live from OpenAlex

This dissertation was undertaken in an effort to determine the efficacy of the current legal system procedures designed to remedy the presence of bias, and specifically racial prejudice, in our jury selection process. These procedures known as the challenge for cause were systematically examined over the course of three studies. The first study conducted in this series was done in the laboratory and showed evidence that the challenge for cause is ineffective in identifying and rejecting biased jurors. The second study was an observational study conducted at the Toronto Superior Court of Justice in which the challenge for cause procedures were examined in their actual practice. During the course of this study a naturalistic variable presented itself for consideration. Specifically, it was observed that during the challenge for cause process the jury pool was either allowed to remain in court for the entire procedure or the jury pool remained outside the court and were called in one by one to be asked the challenge for cause question. The results of this study suggest that when the jury pool remains inside the court, jury pool members are less likely to admit to being prejudiced, and there are less overall rejections. The third study, also conducted in the laboratory, was done to test this naturalistic variable and revealed evidence that the presence or absence of the jury pool during the challenge for cause is an influential variable on the admission of prejudice and the rejection of potential jurors.

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)0.007
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0030.000
Research integrity0.0010.001
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 head0.120
GPT teacher head0.463
Teacher spread0.343 · 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.

Study designQualitative
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
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueTSpaceSame topicJury Decision Making ProcessesFrench-language works237,207