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Record W4280614832 · doi:10.1177/00938548221093283

A Preliminary Exploration of the Multimedia Principle’s Applicability for Improving Comprehension of Youth Interrogation Rights

2022· article· en· W4280614832 on OpenAlexafffund
Christopher J. Lively, Brent Snook, Kirk Luther, Meagan I. McCardle, John C. House

Bibliographic record

VenueCriminal Justice and Behavior · 2022
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsCarleton UniversitySt. Francis Xavier UniversityMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Psychological Association
KeywordsInterrogationAnimationComprehensionPresentation (obstetrics)MultimediaComputer sciencePsychologyMedicineComputer graphics (images)Political scienceLaw

Abstract

fetched live from OpenAlex

We examined the extent to which presenting youth interrogation rights using different combinations of three multimedia elements (Animation, Audio, and Caption) improved comprehension. A 2 (Animation: Present, Absent) × 2 (Audio: Present, Absent) × 2 (Caption: Present, Absent) between-participants design was employed using samples of adults (Experiment 1: N = 207) and youth (Experiment 2: N = 193). Participants in both experiments were shown one of eight multimedia presentations and asked about their understanding of the presented youth interrogation rights. In both experiments, the multimedia presentation that contained animation and caption led to the highest level of comprehension. Implications of these findings for protecting youth and the use of technology during interrogations are discussed.

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)0.029
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 score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.102
GPT teacher head0.377
Teacher spread0.275 · 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
Published2022
Admission routes2
Has abstractyes

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