Indigenous (her)oes and their healing work: ending violence against Indigenous women and girls
Bibliographic record
Abstract
This thesis explores strategies thirteen community leaders are implementing to prevent violence and educate the public on historical and current violence, and to empower Indigenous women and girls. These strategies that the researcher explores are from programs and events in London and Sudbury, Ontario. Through qualitative interviews, the researcher brings together the voices of 13 participants, 11 Indigenous leaders (Anishinaabe and Haudenosaunee), one settler ally, and two Elders, who implement nine initiatives. Using the framework of the Jingle Dress, the researcher ensures that the data is inclusive of culture and Indigenous perspective. The collective strategies were found to have eight main themes: Culture; Education; Oshkabewis: Taking care of Spirit Through Commemoration; Partnerships; Looking Towards the Future; Families; Art as a Medium for Healing; and Funding. The target audience of the initiatives were both Indigenous and non-Indigenous Peoples. This research will help other organizations, grassroots or government, incorporate Indigenous Peoples’ voices and culture within programming and events, and can inform allies on how to decolonize their relations to help improve the well-being of all Indigenous Peoples, as well as improve Indigenous and non-Indigenous relations.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".