PREDICTING ADULTS’ APPROVAL OF PHYSICAL PUNISHMENT FROM THEIR PERCEPTIONS OF THEIR CHILDHOOD EXPERIENCES
Bibliographic record
Abstract
Most physical violence against children in their homes is rooted in physical punishment. Parents’ approval of physical punishment is a primary predictor of its use. Therefore, reducing approval of physical punishment is critical to preventing physical violence against children. We explored the relative contributions of four variables to young adults’ approval of physical punishment with the aim of identifying effective routes to prevention. The participants were 480 first-year university students in 3 Canadian provinces. The outcome measure was a scale assessing participants’ approval of physical punishment. The predictor variables were four dimensions of participants’ perceptions of their childhood physical punishment experiences: physical (frequency, severity), cognitive (perceived abusiveness, perceived deservedness), affective (short- and long-term emotional impact), and contextual (degree to which it was accompanied by reasoning, power assertion, emotional abuse, or emotional support). Most (73%) of the participants had experienced physical punishment in childhood. Of these, 78% had experienced punishments other than mild spanking with the hand; one fifth had been pushed against a wall, and one third had been hit with objects. The strongest predictor of participants’ approval of physical punishment was a belief that their experiences were deserved. Reducing approval of physical punishment requires strategies to alter the perception that children deserve violence.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".