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Record W2116208975 · doi:10.1177/0011128712453689

Measuring the Reading Complexity and Oral Comprehension of Canadian Youth Waiver Forms

2012· article· en· W2116208975 on OpenAlexaffabout
Joseph Eastwood, Brent Snook, Kirk Luther

Bibliographic record

VenueCrime & Delinquency · 2012
Typearticle
Languageen
FieldComputer Science
TopicText Readability and Simplification
Canadian institutionsMemorial University of NewfoundlandBishop's University
Fundersnot available
KeywordsWaiverReadabilitySentenceComprehensionReading (process)PsychologySample (material)LinguisticsComputer scienceLawPolitical scienceNatural language processing

Abstract

fetched live from OpenAlex

The reading complexity of a sample of Canadian police youth waiver forms was assessed, and the oral comprehension of a waiver form was examined. In Study 1, the complexity of 31 unique waiver forms was assessed using five readability measures (i.e., waiver length, Flesch–Kincaid grade level, Grammatik sentence complexity, word difficulty, and word frequency). Results showed that the waivers are lengthy, are written at a relatively high grade level, contain complex sentences, and contain difficult and infrequent words. In Study 2, high school students ( N = 32) were presented orally with one youth waiver form and asked to explain its meaning. Results showed that participants understood approximately 40% of the information contained in the waiver form. The likelihood of the rights of Canadian youths being protected and the need to create a standardized and comprehensible waiver form 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)0.002
metaresearch head score (Gemma)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.499

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.172
GPT teacher head0.281
Teacher spread0.109 · 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

Citations25
Published2012
Admission routes2
Has abstractyes

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