Does adolescent incivility longitudinally predict future bullying?
Bibliographic record
Abstract
INTRODUCTION: Adolescent bullying is a complicated behavior that is difficult to prevent. Understanding factors that predict bullying during adolescence can help us minimize such behavior. Classroom incivility is a low-level antisocial behavior that has been discussed in the literature as being a potential predictor of bullying in adolescence. Therefore, the goal of the present study was to examine the longitudinal link between classroom incivility and bullying. METHODS: Data for the current study was collected using quantitative surveys at two-time points, three years apart (November 2019 and November 2022) in southern Ontario, Canada. Our sample comprised 349 adolescents (51.3% boys, 46.4% girls, 0.6% other, and 1.7% preferred not to say) between the ages of 9 and 14 years old (M = 11.92 years; SD = 1.42). We utilized cross-lagged analyses to examine the stability of classroom incivility in adolescence, and the longitudinal association between classroom incivility and bullying. RESULTS: Classroom incivility at Time 1 predicted bullying behavior at Time 2, while bullying at Time 1 did not predict classroom incivility at Time 2. Our results not only support the stability of levels of classroom incivility across time, but also provide empirical support for classroom incivility as a precursor to bullying behavior. CONCLUSION: Our study suggests that classroom incivility can not only negatively impact the learning environment but may also be implicated in contributing to the circumstances that promote bullying behavior in adolescence, highlighting the importance of limiting uncivil behavior before it escalates into more severe forms of behavior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".