Indicium ex Machina: Unstructured Sentencing and Disparate Outcomes in Canada
Bibliographic record
Abstract
Recent advancements in artificial intelligence have caused a wave of technological normality. As expected, the criminal justice system, and more increasingly, criminal sentencing is seeing a trend of “technosolutionism” due to real concerns about unjustified disparity. Truly, artificial intelligence has the prospect of making the sentencing process more effective, value-driven, consistent, and predictable. However, relying on the assumption that using such a system may require being confined to the normative sentencing traditions of each country, this thesis argues that there are crucial questions to be addressed about how this technological normality fits within the normative pillars of extant legal principles, especially in an anomalous sentencing jurisdiction like Canada. Despite sufficient incentives to integrate AI, the lack of a meaningful sentencing structure significantly undermines the prospect of AI mitigating disparity. To effectively harness the potential of an automated system, the current sentencing approach must substantially shift direction towards a well-structured sentencing practice.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".