Bibliographic record
Abstract
In the first lecture of our Mini Law School series, we look at dilemmas in Canadian sentencing law. What we see on the news tends to be the 'bottom line' of the judge's explains Professor Kaiser. What we don't often have access to are the full reasons given by the court, and this can be really frustrating for members of the public who may perceive a sentence as being 'too lenient'. But sentencing isn't as easy as you might think! You can't just say 'lock them up and throw away the key'—among other things, you have to consider the protection of society and how that is best achieved, the pain of victims, the rehabilitation and reintegration of most offenders, and the messages that the sentence sends. In my lecture, I'll review the context and the basic law on sentencing and then present some fairly typical scenarios to get participants thinking about what would be an appropriate sentence, given the law and our legal system, and we'll work through how a decision must be rationalized before it's handed down.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.072 | 0.023 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".