Advancing legal literacy: The effect of listenability on the comprehension of interrogation rights
Bibliographic record
Abstract
Purpose To examine the effect of listenability features on the comprehension of interrogation rights. Method In Experiment 1, students ( N = 76) underwent a mock interrogation where one of two police cautions (listenable caution vs. standard caution) was administered and students were asked to explain the caution in their own words. Experiment 2 ( N = 80) extended Experiment 1 by identifying the individual and additive effects of the listenability features on recall of their interrogation rights. Results The results of Experiment 1 showed that the caution containing listenability features produced higher levels of recall than a standard caution. Results of Experiment 2 showed that repeating and organizing interrogation rights led to the greatest number of legal rights being comprehended. Conclusions Listenability can be used as a tool to increase legal literacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".