The medium behind the message : an examination of the delivery method of victim impact statements in Canada
Bibliographic record
Abstract
The Medium Behind the Message: An Examination of the Delivery Method of Victim ImpactStatements in Canada By Samantha Webb Victim impact statements (VIS) allow victims to express how a crime has affected them physically, psychologically, and financially.During the COVID-19 pandemic, many sentencing trials were moved online.This study examined victims' experiences with VIS and the justice system, evaluating if the presentation medium affects sentencing.Study 1 interviewed victims via Zoom, revealing they find the process more comfortable and accessible online but prefer inperson or written submissions.Study 2 assessed participants' reactions to real VIS presented in video, audio, or transcript formats.Results showed the medium did not affect victim-related factors or sentence length.However, participants recommended alternatives to incarceration (e.g., probation) more often after reading a VIS, particularly in stalking cases.The type of crime also influenced perceptions; a VIS detailing sexual assault led to higher harm ratings and longer sentences compared to stalking.These findings have implications for victims, judges, and the justice system.
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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 | 0.014 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".