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Record W2107353575 · doi:10.1177/1098300711403153

Kindergarten Reading Skill Level and Change as Risk Factors for Chronic Problem Behavior

2011· article· en· W2107353575 on OpenAlexaff
Kent McIntosh, Carol Sadler, Jacqueline A. Brown

Bibliographic record

VenueJournal of Positive Behavior Interventions · 2011
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyReading (process)Phonological awarenessResponse to interventionEarly literacyDevelopmental psychologyIntervention (counseling)At-risk studentsLiteracySet (abstract data type)Early childhoodLogistic regressionBehavior changeLongitudinal studyMathematics educationSpecial educationPedagogySocial psychology

Abstract

fetched live from OpenAlex

In this study, the authors explored the effect of prereading skills at the start of kindergarten and change in skills during kindergarten on response to Tier I (universal) Schoolwide Positive Behavior Support in Grade 5. A longitudinal data set of 473 students, including Dynamic Indicators of Basic Early Literacy Skills measures at the start, middle, and end of kindergarten and office discipline referrals in Grade 5, was used to determine whether reading skills at school entry or change in reading skills over the course of kindergarten were more predictive of chronic problem behavior in Grade 5. Results of logistic regression analyses indicated that low initial phonological awareness predicted problem behavior, but including skill growth in the model resulted in significantly improved and more accurate prediction. Results are discussed in terms of early screening and intervention and reducing risk for problem behavior through quality Tier I reading instruction in kindergarten.

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)0.005
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.485
GPT teacher head0.431
Teacher spread0.053 · 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

Citations73
Published2011
Admission routes1
Has abstractyes

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