Extending a relative methodological perspective to sentencing outcome analysis
Bibliographic record
Abstract
The mood and temper of the public in regard to any issue ought to be informed by up to date, comprehensive, valid, and reliable information. With respect to sentencing, the Canadian public has never been well-informed. This thesis suggests that introducing alternative methodological perspectives may hold the key to unlocking new findings in existing data sources. This is particularly true for descriptive comparison procedures where the goal is to identify meaningful patterns across factors related to the sentencing process. In order to supplement direct comparative procedures that have been used in previous research, this thesis uses a relative methodological perspective to develop new measurement techniques. A compilation of three studies employs the new techniques with existing data available in Canada to study critical areas of inquiry that have long plagued sentencing in the country. Study 1 introduces an analytic method to explore national patterns of sanction use across a series of offence categories. The new technique serves as an important supplement to conventional measures by uncovering patterns that had previously gone undetected. Study 2 uses the general approach proposed in Study 1 to advance a more complex analytic technique to detect jurisdictional consistency in sentencing outcomes. The technique is found to identify new forms of sentence consistency and disparity that had been neglected in previous research. Study 3 uses the strategy employed in Study 2, to study the sentencing patterns of Aboriginal offenders, specifically. By employing conventional measures alongside the newly developed technique, the study demonstrates that certain provinces and territories are disproportionately represented in their patterns of correctional program use with Aboriginal offenders. Collectively, the results of this thesis highlight the importance of adopting a relative perspective in sentencing outcome analysis.
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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 | 0.416 | 0.532 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".