The effect of child sexual exploitation images collection size on offender sentencing
Bibliographic record
Abstract
Although image analysis techniques are improving, classifying images collected from the hard drives of individuals consuming Child Sexual Exploitation Material (CSEM) is both time-consuming and costly. Police organizations offer two main motives for the systematic analysis of these images: 1) to identify victims of sexual abuse and 2) to determine the number of illegal images as large numbers of such images lead to longer prison sentences. This study examines the assumption behind the second motive by analyzing connections between the number of images and the sentences imposed by Quebec courts. Results of quantitative analysis show that sentence length is most affected by the offender’s criminal history or present activity and that for the majority of cases the number of images in the collection has no impact on the prison term. Results of qualitative analysis of judges’ written decisions in cases of accusation of possession of CSEM show that they emphasize three aspects: 1) the size of the collection, 2) the time spent managing the collection, which is seen as a measure of deviance and motivation, and 3) the nature of the images. The current study offers a better understanding of the factors that affect decisions in CSEM cases.
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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 | 0.008 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".