Victim impact statements at sentencing: Towards a clearer understanding of their aims
Bibliographic record
Abstract
The aims of victim impact statements (VIS) can be classified into two main categories – instrumental and expressive. These different sorts of aims are associated with different, and often conflicting, sentencing objectives. This article argues that the VIS regime in Canada remains a legal no man’s land, with neither its role nor its aims being clearly defined and articulated. Indeed, recent appellate court decisions have shown a number of inconsistencies and conflicts in the instrumental and expressive purposes that VISs in Canada are meant to serve. Further, it is also argued, the proposed legislative amendments under Bill C-32 are not very promising, since this scheme also fails to clearly articulate the aims and rationales behind the statements and behind the proposed changes. It is shown, throughout the article, that VIS regime guidelines and parameters can take different shapes and forms, depending on the aims retained. Moreover, while a dualist scheme that reconciles instrumental and expressive aims may be possible, clarity would be necessary in order to craft adequate parameters. Certainly, more protective measures are necessary if instrumental aims are to be retained. Finally, having laid out the conceptual and foundational grounds required to understand the possible aims of VISs and how these different aims can shape the relevant parameters, the article proceeds by laying out an initial, more normative, proposal for a VIS multi-functional model inspired by evidence-based findings.
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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 | 0.037 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.026 | 0.022 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.010 |
| 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".