Confidence, Training, and Barriers for Canadian Law Enforcement in Cases of Luring, Sexual Abuse, and Child Sexual Abuse Imagery
Bibliographic record
Abstract
The exponential growth of Internet access has generated new platforms for child sexual abuse. The Internet serves as a platform for luring children to engage in sexual activity and also for accessing, distributing or producing sexually explicit material featuring children (“child sexual abuse imagery” (CSAI)). Both types of offenses have been criminalized in various jurisdictions around the world, compelling law enforcement professionals to detect, identify, investigate, and refer such offenses for prosecution. This study was conducted to understand law enforcement training, confidence, and barriers to responding to three categories of criminal activity involving the online sexual exploitation of children: (1) luring, (2) creation and distribution of CSAI, and (3) sexual abuse. This study surveyed two separate police units responsible for investigating sex crimes and/or child abuse in two large Canadian cities. More participants reported encountering a suspected or confirmed case of child sexual abuse within the last year than a case of luring or CSAI (57%, 45%, and 35%, respectively). Participants indicated they had received more formal training on investigating sexual abuse than luring or CSAI and felt less confident in their ability to investigate crimes involving technology and online platforms than other types of investigations. Participants were also more likely to encounter barriers relating to the “soft skills” of working with youth and their families relative to the “hard skills” of investigating criminal activity. Due to the exponential growth of online sexual abuse and the need for nimble and technologically savvy investigative responses, law enforcement should receive more formal training in all areas of online child sexual exploitation, including the criminal offenses of luring and CSAI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".