MétaCan
Menu
Back to cohort

Jury Selection and Voir Dire

2015· other· en· W1939006902 on OpenAlex

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

VenueThe Encyclopedia of Clinical Psychology · 2015
Typeother
Languageen
FieldSocial Sciences
TopicJury Decision Making Processes
Canadian institutionsCarleton University
Fundersnot available
KeywordsJuryJury selectionStatutory lawSelection (genetic algorithm)LawConstitutionHung juryPolitical scienceJury instructionsPsychologyProcess (computing)Computer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract The right to a fair trial by an impartial jury of one's peers is guaranteed under the 6th and 14th Amendments of the United States' Constitution. Jury selection is accomplished through voir dire (“to speak the truth”), a process where potential jurors are questioned by attorneys and/or the judge in order to ascertain their suitability to serve on the jury. The goal of voir dire is to eliminate potential jurors who are ineligible due to statutory regulations, insurmountable biases, or refusal to apply the law as it is written. In the past 30 years, scientific jury selection (SJS), the process of applying scientific research methodology and psychological theory to jury selection, has become increasingly prevalent.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.121
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.162
GPT teacher head0.543
Teacher spread0.381 · 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