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Record W4410206365 · doi:10.1080/10511253.2025.2500316

The Jury is in: An Evaluation of an Experiential Court Assignment

2025· article· en· W4410206365 on OpenAlexaff
Josh Bullock, Brandon Sparks

Bibliographic record

VenueJournal of Criminal Justice Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methodologies in Social Sciences
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsJuryExperiential learningLawCriminologyPsychologyCriminal justicePolitical science

Abstract

fetched live from OpenAlex

This study evaluates the effectiveness of an experiential learning assignment designed for criminology and forensic psychology students, requiring them to attend a Crown Court trial in the public gallery or to engage with a virtual mock trial. 48 students were surveyed to measure the impact of experiential assignments in helping students better understand the module content, the criminal justice system and if the experience increased their motivation to continue with their course. Findings indicate strong student support for the assignment, with 81.3% stating it as valuable and 79.2% wanting more experiential learning opportunities in their criminal justice related courses. We found that in-person experiences received slightly higher student ratings, however, both in-person and virtual contributed positively to learning outcomes. We highlight the importance of experiential learning in improving student engagement, and real-world application of their degree.

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)0.008
metaresearch head score (Gemma)0.036
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.036
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.191
GPT teacher head0.545
Teacher spread0.354 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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