More Arguments for the Weakness of the Empirical Evidence Used to Support Spanking Bans: Rejoinder to Afifi et al. (2025) and Kraus de Camargo (2025)
Bibliographic record
Abstract
In this rejoinder we address 13 concerns elicited by our invited commentary “An update on the scientific evidence for and against the legal banning of disciplinary spanking.” In addition to defending assertions made in the initial commentary, we make several new substantive arguments. In response to dissenters’ equating of non-experimental evidence against spanking with non-experimental evidence against smoking, we demonstrate that the two are very dissimilar. We question the purpose of spanking bans, providing stronger evidence that they do not seem to prevent child abuse. We review Canada’s association with the UN’s Convention on the Rights of the Child (CRC) before and after the 2006 classification of all physical punishment as violence. We discuss the disciplining of children with disabilities. We encourage fellow researchers to avoid the scholar-advocacy bias, appropriately discriminating methodological evaluations of empirical evidence from personal convictions.
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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 | 0.181 | 0.525 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.014 | 0.009 |
| Research integrity | 0.072 | 0.089 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".