Moving toward improving the delivery of youth interrogation rights: can comprehension be enhanced through multimedia?
Bibliographic record
Abstract
Across three experiments, the extent to which presenting youth interrogation rights in a multimedia format using three multimedia elements (Animation, Audio, and Caption) improved comprehension was examined. Experiments 1 and 2 employed a 2 (Animation: Present vs. Absent) X 2 (Audio: Present vs. Absent) X 2 (Caption: Present vs. Absent) between-participants design with samples of Canadian adults (N = 207) and youth (N = 193), respectively. Participants in both experiments were randomly shown one of eight multimedia presentations and then tested about their understanding of the youth interrogation rights content contained in the multimedia presentation. In both experiments, the multimedia presentation showing Animation and Caption yielded the highest comprehension score. Experiment 3 carried out a single-condition design with Canadian youth (N = 60) to collect opinions about the multimedia elements used in the stimuli. Participants were presented with a multimedia presentation containing all three multimedia elements (i.e., Animation, Audio, and Caption) and asked to provide their feedback about the presentation more broadly (e.g., evaluating the quality, rate of speed, distraction level, and their ability to identify character within the presentation); positive reviews were reported by nearly all participants. Implications of these collective findings for protecting youth and the use of technology during police interrogations are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".