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Record W2273893092 · doi:10.1177/0734282915623446

Measuring Adolescent Attitudes Toward Classroom Incivility

2015· article· en· W2273893092 on OpenAlexaff
Ann H. Farrell, Daniel A. Provenzano, Natalie Spadafora, Zopito A. Marini, Anthony A. Volk

Bibliographic record

VenueJournal of Psychoeducational Assessment · 2015
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsBrock University
Fundersnot available
KeywordsPsychologyIncivilityConfirmatory factor analysisExploratory factor analysisScale (ratio)Construct validitySample (material)Construct (python library)Developmental psychologyPsychometricsSocial psychologyClinical psychologyStructural equation modeling

Abstract

fetched live from OpenAlex

The purpose of this study was to develop a scale that measures adolescents’ attitudes toward classroom incivility and determine whether items would reveal subscales. A sample of 549 adolescents between ages 11 and 18 (53.1% boys; M age = 13.90, SD = 1.41) completed items written to measure attitudes toward classroom incivility. An exploratory factor analysis (EFA) was used on one half of the randomly split sample and a confirmatory factor analysis (CFA) on the remainder. Results from both analyses suggested that two factors representing unintentional and intentional incivility might be the best factor solution. In addition, evidence for concurrent validity was found in correlations with four additional scales. Results suggest that attitudes toward classroom incivility are heterogeneous and that adolescence may be an important developmental period to address this construct. Future studies should continue psychometrically developing this scale and exploring this measure with additional antisocial beliefs and behaviors.

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 score0.002
Threshold uncertainty score0.007

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 science0.0000.000
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 head0.127
GPT teacher head0.409
Teacher spread0.282 · 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

Citations22
Published2015
Admission routes1
Has abstractyes

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