26. All That Glitters Is Not Gold: The False Promise of Victim Impact Statements
Bibliographic record
Abstract
This chapter interrogates whether or not the criminal justice system holds potential for fairly representing women’s experiences of harm while affirming their dignity, equality, and autonomy. Specifically, Rakhi Ruparelia questions the opportunity to present a victim impact statement (VIS) to the judge who is sentencing a sex offender. While not opposing a criminalization strategy, as do Alison Symington and Julie Desrosiers in the specific contexts discussed in their respective chapters, Rakhi expresses similar skepticism that the law permitting the filing of a VIS is actually premised on deeply conservative ideologies regarding who are real and what their proper role in the criminal justice system is. Like the Sexual Assault Evidence Kit originally touted as a positive development for women, the VIS is more likely to be used to discredit women’s claims than to validate them when it comes to sexual assault. Rakhi explores systemic racism in sentencing and argues persuasively that Aboriginal and racialized men will bear the brunt of VIS use and that Aboriginal and racialized women have little if anything to gain from the VIS. The VIS, she argues, is really about appeasing victims and maintaining the individualized focus of the criminal 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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.049 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".