Bibliographic record
Abstract
Statistics Canada (2009) indicates Indigenous women are at the highest risk of intimate partner violence (IPV) as they experience it at rates three times higher than others. Research on this topic is often detached from the community, thereby, limiting women's ability to assert their voices. There also remains crucial gaps in knowledge on factors that attribute to Indigenous women ending the cycle of IPV. Thus, this research aims to create space for Indigenous women to share their stories and voice their own reflections on the process of how they ended IPV in their lives, in a way that is more empowering and meaningful. Recruitment was conducted through snowball sampling, partaking in community events, and by sending out posters and letters of invitation to organizations. Using a traditional Indigenous practice within a collaborative focus group narrative design, a sharing circle was facilitated with a group of five Indigenous women over the age of 18. In the circle women shared their stories, engaged in discussion, and participated in a oral analysis of the themes in their individual stories, as well as the collective narratives. The identification of themes by the participants themselves, allowed for the participants voices' to be expressed within the results of the research itself. Following this, a secondary six-step thematic analysis was conducted by the researcher in order to situate the data within the themes as described by the participants. All findings were reported back to participants for validity checks to ensure collaboration in all stages of the research. Results of this research will ultimately inform counselling and other professional practices as it will add to the foundation of knowledge needed in order for the resolution of IPV against Indigenous women.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.017 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".