Measuring Reading Complexity and Listening Comprehension of Canadian Police Cautions
Bibliographic record
Abstract
The reading complexity and listening comprehension of Canadian police cautions were measured. In Study 1, the complexity of 44 unique Canadian police cautions was assessed using five readability measures (Flesch-Kincaid reading level, sentence complexity, use of difficult words, use of infrequent words, and number of words). Results showed that 7 (37%) of the right-to-silence cautions ( n = 19) and none of the right-to-legal-counsel cautions ( n = 25) reached acceptable cutoff levels for all five measures. In Study 2, university students ( N = 121) were presented with one of three cautions verbally and were asked to explain its meaning. Despite variations in complexity across the three cautions, participants understood approximately one third of the information contained in the cautions. The extent to which the needs of Canadian suspects and police organizations are being met and the validity of reading complexity as a predictor of listening comprehension are discussed.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".