Preventing Punitive Violence: Preliminary Data on the Positive Discipline in Everyday Parenting (PDEP) Program
Bibliographic record
Abstract
Most physical violence against children is punitive in intent. The United Nations has called for the elimination of physical punishment of children and for the development of programs teaching nonviolent resolution of parent-child conflict. A focused effort is required to shift entrenched, intergenerationally transmitted, and culturally normalized belief systems about physical punishment. Positive Discipline in Everyday Parenting (PDEP) was developed to meet this need. Its short-term objectives are to: 1) reduce approval of physical punishment; 2) normalize parent-child conflict; and 3) strengthen parenting self-efficacy. PDEP was delivered by trained program facilitators to 321 parents living in 14 cities in Canada. Responses to pre and posttest questionnaires suggest that parents who completed postprogram measures were less likely to both approve of physical punishment and view typical parent-child conflict as misbehaviour on the part of the child, and also to have greater parenting self-efficacy. More than 90% believed more strongly that parents should not use physical punishment, and that PDEP would help them control their anger and build stronger relationships with their children. PDEP is a promising approach to the prevention of punitive violence against children.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".