Learning From Those on the Ice: The Impact of Bill C-75 on Nunavummiut
Bibliographic record
Abstract
On March 29th, 2018, the Liberal Government introduced Bill C-75, which received Royal Assent on June 21st, 2019. The sweeping legislation has implemented various amendments throughout the Criminal Code, including provisions targeted at addressing intimate partner violence (IPV). One such amendment has sparked criticism: the introduction of a reverse onus at bail for an accused charged with a violent offence against an intimate partner if they have a prior conviction for a similar offense. Through qualitative interviews undertaken with seven Nunavut lawyers, this research considers the impact of Bill C-75, specifically the reverse onus in cases of IPV, on Nunavummiut. The paper argues that the introduction of the reverse onus will not only disproportionately and detrimentally affect Nunavummiut accused, it will simultaneously fail to keep complainants and society safer. In effect, “tough on crime” mentalities will continually perpetuate IPV in Nunavut. This paper urges its readers to think about solutions to IPV for Nunavummiut in a holistic manner, looking outside the criminal justice system. Through Inuit Qaujimajatuqangit, empowering communities is the first step to addressing IPV, improving well-being, and ensuring the protection of human dignity.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.040 | 0.017 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".