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Record W2145561667 · doi:10.1177/0093854810362053

Measuring Reading Complexity and Listening Comprehension of Canadian Police Cautions

2010· article· en· W2145561667 on OpenAlexaffabout
Joseph Eastwood, Brent Snook, Sarah J. Chaulk

Bibliographic record

VenueCriminal Justice and Behavior · 2010
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsActive listeningReadabilitySentencePsychologyMeaning (existential)Reading (process)ComprehensionReading comprehensionListening comprehensionSocial psychologySilenceApplied psychologyLinguisticsComputer scienceCommunicationNatural language processing

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.935
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.158
GPT teacher head0.361
Teacher spread0.202 · 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 teacher head, 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

Citations26
Published2010
Admission routes2
Has abstractyes

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