Beginning with Our Voices: How the Experiential Stories of First Nations Women Contribute to a National Research Project.
Bibliographic record
Abstract
The purpose of this paper is to review how the experiential stories of First Nations women contribute to a national research project. The project focuses on how women's healing is impacted by their views about themselves as - and the stigma associated with being - a drug user, involved in crime and an Aboriginal woman. Our project began with three First Nations women on our research team documenting the role of stigma and self-identity in their personal healing journeys from problematically using drugs and being in conflict with the law. In this paper we discuss how key components of feminist research practices, Aboriginal methodology and community-based research helped us position the women's experiential stories in authoritative, recognized and celebrated ways in our study. We illustrate how the women's stories uniquely contributed to the creation of our interview questions and the research project in general. We also discuss how the women personally benefited from writing about and sharing their experiences. Key benefits include the women discovering the impact of the written word, promotion of their healing, personal recognition of their ability to offer hope to women in need, increased self-esteem, and increased appreciation of the importance of sharing their lived experiences with others. Our method of research differs from a conventional western scientific approach to understanding, and as such made important contributions to both the project itself and the women who shared their experiential stories.
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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 | 0.032 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.037 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".