MétaCan
Menu
Back to cohort
Record W2170742296 · doi:10.1177/0022466907313253

Reading Skills and Function of Problem Behavior in Typical School Settings

2008· article· en· W2170742296 on OpenAlexaff
Kent McIntosh, Robert H. Horner, David J. Chard, Celeste Rossetto Dickey, Drew Braun

Bibliographic record

VenueThe Journal of Special Education · 2008
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFluencyPsychologyReading (process)Multivariate analysis of varianceDevelopmental psychologyFunction (biology)General educationSpecial educationVariance (accounting)Mathematics educationStatistics

Abstract

fetched live from OpenAlex

The authors examined the relation between variables maintaining problem behavior and reading performance for elementary-age students. Participants were 51 students in Grades 4, 5, and 6 who had received two or more office discipline referrals in 2003-2004. Students were grouped by teacher-indicated function of problem behavior. The prevalence of behavioral function for students in general and special education is reported, and differences were determined for the number of discipline referrals and oral reading fluency rates. Chi-square analysis indicated differences in base rates of function between students in general and special education. Multivariate analysis of variance indicated significant differences in oral reading fluency by function of problem behavior, and follow-up analyses indicated significantly lower fluency scores for students whose indicated function was escape from academic tasks. These findings provide evidence for a coercion model in the classroom. The results are discussed in terms of the relevance of using functional behavior assessment and behavior support with general education populations.

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)0.001
metaresearch head score (Gemma)0.008
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.064
GPT teacher head0.341
Teacher spread0.278 · 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

Citations65
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Special EducationSame topicBehavioral and Psychological StudiesFrench-language works237,207