How the Canadian Government Continues to Enable Violence Against Indigenous Women and Girls
Bibliographic record
Abstract
Most Canadians are aware of the injustices Indigenous women and girls have faced and continue to face. However, their lack of understanding of how the Canadian Government, a supposed proud enthusiast of multiculturalism and cohabitation, deliberately neglects this community and ensures a consistent increase of Missing and Murdered Indigenous Women and Girls in Canada. This research aims to demonstrate the government’s position as both a facilitator and of outreach solutions and a contributor to their deaths. In order to effectively illustrate the government’s involvement, this essay will examine the historical conditions which led to Indigenous women’s perpetual state of inequality, the methods of eradication which further perpetuate Indigenous women’s vulnerability to violence, and Indigenous solutions and methods of eradicating gendered violence. It is crucial that this topic continue to be pursued, as Indigenous women and girls are consistently dying from entirely preventable causes. Overall, the research proves that Indigenous women are not safe in the care of the Canadian government, as Canada still denies that colonialism is enacted in all spheres of government.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.042 | 0.017 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".