Reciprocal associations between teachers' use of disciplinary practices and aggression in elementary school students
Bibliographic record
Abstract
Transactional theories of human development suggest that the association between teachers' disciplinary practices and students' aggressive behavior may be reciprocal. However, no study has tested this possibility. Therefore, this study examines reciprocal associations between teachers' use of disciplinary practices (educational and punitive) and aggressive behaviors in elementary school students. A sample comprising 1038 students (62 % boys) was assessed at the start and end of the kindergarten year and annually from grades one to four. At each assessment, teachers reported how frequently they used disciplinary practices with each participating student and completed a measure of the aggressive behaviors of these students. Results of a latent curve model with structured residuals (LCM-SR) revealed that higher-than-usual levels of teacher-reported kindergarten students' aggressive behaviors in the fall predicted higher-than-usual levels of teacher-reported punitive practices in the spring. Moreover, higher-than-usual levels of kindergarten teachers' punitive practices in the spring predicted higher-than-usual levels of students' aggressive behaviors in grade one. In the following years, no other reciprocal influences were found between punitive practices and aggression. Moreover, using educational disciplinary practices did not lead to a decrease in aggression. The results underscore the need to equip teachers with the skills to manage disruptive classroom behaviors, particularly in kindergarten and during the transition to grade one, to prevent aggressive behaviors from spiraling downward.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".