Towards the development of a culturally sensitive, empowerment-based sexual assault resistance model for Anishinaabe women
Bibliographic record
Abstract
In Canada, sexual violence against Indigenous women is an unsettling, national human rights crisis. Conservative estimates are that 57% of Indigenous women have been sexually assaulted (Native Women’s Association of Canada, 2011). Compared with non-Indigenous women they experience three times more intimate partner violence, suffer from more extreme violence and are targeted by both Indigenous and non-Indigenous men (Boyce, 2016). The House of Commons, Special Committee on Violence Against Women in 2014 emphasized the need for the development of education and prevention programs to address violence against Indigenous women (Ambler, 2014). Sexual violence against Indigenous women intersects with historic genocide, intergeneration trauma, entrenched racism, sexism, and poverty. Although some sexual assault resistance programs have been found to lessen sexual assaults by 50% (Orchowski & Gidycz, 2018), none have been developed that address the unique history, culture, and needs of Indigenous women. This thesis explores the development of a culturally sensitive, empowerment-based sexual assault resistance model and preliminary program for Anishinaabe women. The development was informed by peer-reviewed literature and in collaboration with two Anishinaabe elders. The emergent model and preliminary program were reviewed by a focus group of three professional Anishinabek helpers. This study is a step towards lessening sexual violence against Indigenous women in Canada, supports social work competence and practice for working with Indigenous women, and furthers sexual violence prevention efforts in Ontario’s north.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".