Swedish Trends in Criminal Assaults against Minors since Banning Spanking, 1981-2010
Bibliographic record
Abstract
This study uses Swedish trends in alleged criminal assaults against minors to investigate whether societal violence has decreased since their spanking ban in 1979.The rates of all assaults increased dramatically. Compared to 1981, criminal statistics in 2010 included about 22 times as many cases of physical child abuse, 24 times as many assaults by minors against minors, and 73 times as many rapes of minors under the age of 15. Although the first cohort born after the spanking ban showed a smaller percentage increase in perpetrating assaults against minors than other age cohorts, those born since the spanking ban had almost a 12-fold increase in perpetrations altogether, compared to a 7-fold increase for older age cohorts. Although some increases might reflect changes in reporting practices, their magnitude and consistency suggest that part of these increases are real. Recent increases may be due to expanding proscriptions against nonphysical disciplinary consequences. Future research needs to identify effective alternative disciplinary consequences to replace spanking. Otherwise, proscriptions against an expanding range of disciplinary consequences may undermine the kind of appropriate parental authority that can facilitate the development of impulse control in oppositional children and appropriate respect for others, especially the physically vulnerable
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".