The links between adult personality, parental discipline attitudes and harsh child punishment
Bibliographic record
Abstract
Why are some adults predisposed to punish children’s misbehavior more severely than others? We examined the role of personality and attitudes about punishment on punishing children’s misbehavior. More specifically, punishment based on intentionality of the misbehavior and the child’s age. Using video stimuli of children’s misbehavior we found lower HEXACO’s Honesty-Humility, Emotionality and Agreeableness were related to favorable attitudes about physical punishment. Additionally, lower Agreeableness was indirectly related to more severe hypothetical punishment, regardless of child age or intention, through the indirect effect of favorable attitudes about physical discipline. Similar findings were found for lower Emotionality, however lower Emotionality was forgiving of the accidental misbehavior. Lower Emotionality (sentimentality and empathy) and Agreeableness (forgiveness and patience) may be at-risk traits for harsher punishment explained through favorable attitudes about physical punishment. Each aspect of the HEXACO’s antisocial traits therefore appears to have a distinct relationship with parental attitudes and hypothetical punishment, which can be informative for at-risk parenting interventions such as differentiating the risks associated with callous versus angry dispositions.Supplemental data for this article is available online at https://doi.org/10.1080/26904586.2021.1957056 .
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".