Getting to Proportionality: The Trouble with Sentencing for Possession of Child Pornography in Ontario
Bibliographic record
Abstract
In this article we examine sentencing in 14 Ontario cases of possession of child pornography between 2007 and 2017 with the purpose of understanding the sentencing process in relation to the fundamental principle of proportionality and other principles employed to arrive at a fair, individualizing process as set out in Canadian sentencing law. In all cases the offenders are charged with possession only and have no prior offences. We situate these cases within the context of sentencing reform in general and child pornography law specifically, including the evolution of mandatory minimums, as they have evolved in both legislation and case law. Our cases cover two periods of mandatory minimums, 45 days and six months. Although we consider numerical sentences, probation and ancillary conditions awarded when examining our cases, we are interested in the process of determining the sentencing components. We analyse this process in two ways: by observing the judicial reasoning in calculating the seriousness of the crime and the blameworthiness of the offender and the balancing of other purposes and principles, particularly rehabilitation and parity; and, by considering three pairings of cases, each with similar quantity and quality of images, to compare the calculation of risk and its effect on determining the blameworthiness of the particular offender. Our findings reveal a polarization in judicial reasoning between a punitive process in which overemphasis of denunciation and deterrence and extreme versions of the reasoned apprehension of harm add weight to the seriousness of the crime on a par with contact abuse, and a more tempered and restrained one in which possession is considered on its own and other purposes and principles are weighed, such as rehabilitation and parity, to arrive at a more individualizing process. Mandatory minimums are no constraint as sentencing is much lengthier, especially under the 45-day mandatory minimum. In pairing like cases in terms of collections of images and videos we find a very subjective process in the calculating of risk in which like offenders are treated differently in terms of assessments of blameworthiness, based on questionable forensic methods and assumptions. Finally, we note the resources involved in investigative time, incarceration and the supervising of probation as well as lengthy ancillary conditions that may last decades after sentencing.
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".