The influence of victim impact statements and offender character on sentencing and parole decisions
Bibliographic record
Abstract
The Influence of Victim Impact Statements and Offender Character on Sentencing and Parole DecisionsBy Kimberley Tirkalas Victims can provide evidence at sentencing and parole hearings that describe the physical, emotional, and financial harm they have suffered due to the offence.Similarly, offenders can provide evidence of their good character or resources that support rehabilitation.This thesis aimed to examine the roles that evidence from victims and offenders have on judicial decision-making at sentencing and parole board outcomes.Study one analyzed 1992 Canadian sentencing decisions and found that victim impact statements and offender character evidence predict incarceration.The effect of victim impact statements was greater when offender evidence was absent than when it was present.Study two examined 55 Parole Board of Canada parole decisions to investigate victim statements and letters of support but found no relationship with parole outcomes.These results provide insight about which variables may influence decision makers in the justice system.Implications for offenders, crime victims, judges, and parole board members are discussed.
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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 | 0.006 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".