Teachers' behaviour and children's academic achievement: Evidence of gene–environment interactions
Bibliographic record
Abstract
BACKGROUND: Children's academic achievement is considerably influenced by genetic factors, which rarely operate independently of environmental influences such as teachers' behaviour. Praise and punitive discipline are commonly used management strategies by teachers. However, their effects on the genetic expression of children's academic achievement are still unclear. AIMS: This study examined potential gene-environment interactions in the associations between children's estimated genetic disposition for academic achievement and teachers' use of praise and punitive discipline in predicting academic achievement. SAMPLE: The participants were 165 twin pairs in sixth grade (M = 12.1 years). METHODS: Teachers reported on children's academic achievement, as well as on their own behaviour. RESULTS: Multilevel regression analyses showed significant interactions between children's estimated genetic disposition for academic achievement and teachers' use of praise and punitive discipline, respectively, in predicting academic achievement. These interactions indicated an enhancement process, suggesting that genetically advantaged children are those most likely to benefit from regular praise and infrequent punishments from their teacher. Moreover, genetically advantaged children were not more (nor less) likely to receive praise or punishments than other students. However, students from underprivileged backgrounds were less likely to receive praise from their teachers. CONCLUSIONS: The results emphasize the importance of teachers' regular use of praise and infrequent punitive discipline to help genetically advantaged children reach their full potential. Future studies should investigate other protective factors of the school environment that might reduce the role of genetic influences that undermine disadvantaged youth's academic achievement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".