Justice as a Rounding Error?: Evidence of Subconscious Bias in Second-Degree Murder Sentences in Canada
Bibliographic record
Abstract
There are few areas of law that grant judges as much discretion as the sentencing of criminal offenders. This discretion necessarily leads to concerns about the influence of biases, including those that result from subconscious processes associated with human cognition; that is to say, heuristics. In this article, the authors explore one heuristic—number preference—through an examination of all reported second degree murder parole ineligibility decisions between 1990 and 2012. Number preference leads individuals to predictably round off measurements to certain favoured numbers. The authors identify a tendency for parole ineligibility decisions to cluster around even numbers and multiples of five, without any obvious, legally-justifiable reason for such rounding. The authors propose that the phenomenon should cause concern not least because it suggests that other, less easily measurable but no less powerful, heuristics may also be at work in judicial decisions.
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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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".