MétaCan
Menu
Back to cohort
Record W1939474120 · doi:10.1002/pits.21742

EVALUATION OF THE CLASS PASS INTERVENTION FOR TYPICALLY DEVELOPING STUDENTS WITH HYPOTHESIZED ESCAPE‐MOTIVATED DISRUPTIVE CLASSROOM BEHAVIOR

2013· article· en· W1939474120 on OpenAlexaff
Clayton R. Cook, Tai A. Collins, Evan H. Dart, Michael J. Vance, Kent McIntosh, Erin A. Grady, Policarpio DeCano

Bibliographic record

VenuePsychology in the Schools · 2013
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyIntervention (counseling)Task (project management)Psychological interventionClass (philosophy)Replication (statistics)Multiple baseline designBehavior changeProsocial behaviorSocial psychologyDevelopmental psychologyApplied psychologyComputer science

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate the Class Pass Intervention (CPI) as a secondary intervention for typically developing students with escape‐motivated disruptive classroom behavior. The CPI consists of providing students with passes that they can use to appropriately request a break from an academic task to engage in a preferred activity for preset amount of time. In addition, students are incentivized to not use the class passes by continuing to engage in the academic task and instead exchanging them for a preferred item or activity. Using an experimental single‐case withdrawal design with replication through a concurrent multiple‐baseline across‐participants design, the CPI was shown to reduce disruptive behavior and increase academic engagement in three students who engaged in hypothesized escape‐motivated behavior. Results also revealed that the effects of the CPI were maintained at a two‐week follow‐up probe and consumers found it to be acceptable. The limitations and implications of the findings for future research on effective classroom‐based interventions 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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.220
GPT teacher head0.427
Teacher spread0.207 · 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

Citations25
Published2013
Admission routes1
Has abstractyes

Explore more

Same venuePsychology in the SchoolsSame topicBehavioral and Psychological StudiesFrench-language works237,207