Aboriginal Women's Increased Rates of Abuse Compared to Non Aboriginal Women Due to Contributing Factors of Poverty, Isolation and Substance Abuse
Bibliographic record
Abstract
In the past 30 years it has been documented that over 520 aboriginal women have either gone missing or have been murdered in Canada (Native Women’s Association). Although Aboriginal women represent only 3% of the Canadian population (Violence Against Aboriginal Women and Girls), they are over represented as victims of racialized, sexualized violence, and are often targeted because of contributing factors that increase their susceptibility to becoming victims of violence. I will be presenting the results regarding how in Canada, aboriginal women experience higher rates of violence and abuse while living on reserves compared to non‐aboriginal women, specifically in regards to how poverty, substance abuse and isolation contribute to the increased rates of violence. Through interrogating these factors, I will provide a reading as to why aboriginal women are more susceptible to higher rates of abuse, so that strategies can be developed to reduce violence and therefore focus on prevention, support and protection for the victims and their families. By researching the contributing factors that increase the rates of violence towards aboriginal women, the social obstacles can then be challenged and changes can be made to the current configurations, decreasing the rates of violence. There are ways in which these factors can be decreased and improvements can be made that will lessen the rates of abuse. With an increase in awareness about these issues in Canada, these problems can be targeted and in time, become problems of the past. 23
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".