The Prevalence of Sexual Abuse by K-12 School Personnel in Canada, 1997–2017
Bibliographic record
Abstract
Studies surrounding the sexual abuse of children by school personnel in Canadian contexts are infrequent and often limited in their scope. The present study addresses this drawback with a contribution of data gathered from disciplinary decisions of educator misconduct, media reports, and published case law concerning child/student sexual abuse cases (between 1997 and 2017) that involved any individual employed (or formerly employed) in a Canadian K-12 school. The study revealed a number of interesting points about the larger student victim and offender demographic patterns and characteristics across Canada. The study found 750 cases involving a minimum of 1,272 students and 714 offenders, 87% of which were male. Moreover, 86% of all offenders were certified teachers, and offenders employed grooming as the main tactic in 70% of the cases. Of the child/student victims, 75% were female, 55% were sexually abused on school property, and more than two-thirds of all victims were in high school at the time the offense was committed. The study also found that excluding Ontario and B.C., the media was the sole source of information for 50-86% of all cases depending on the province/territory. Finally, almost three-quarters of offenders from the study were charged with at least one criminal offense, and of the cases that proceeded to trial, 70% resulted in findings of guilt.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".