Violence and Systemic Injustice: The Effects of Colonialism and Neoliberalism on the Overrepresentation of Indigenous Women and Girls in Canada's Criminal Justice System
Bibliographic record
Abstract
Abstract This chapter examines the relationship between neocolonialism, neoliberalism and the overrepresentation of Indigenous women and girls in Canada's criminal justice system. Indigenous women are 60% more likely to be convicted of violent offences than non-Indigenous women and they make up 42% of all federally sentenced women – while First Nations people represent approximately 5% of the total Canadian population. With an abolition feminist and decolonial theoretical framework, we argue that even when Indigenous women do commit violent crimes, their criminalisation is contingent on the legacy of colonialism. This includes the ongoing genocide against Indigenous women and girls and a neoliberal criminal justice system that reproduces gendered racial state violence and perpetrates the portrayal of stereotypes about Indigenous women, rendering them as inherently violent and ‘risky’. We examine why and how such a disproportionate number of Indigenous women end up involved in cycles of violence, with subsequent disputes with the law. This chapter advances the field of feminist criminology by building on feminist analyses of penal abolition to critique global neoliberalism and the interlocking systems that sustain the ongoing violence in which First Nations women and girls are involved.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".