Stuck at the border of the reserve: Self-idenatity and authentic identity amongst mixed race First Nations women
Bibliographic record
Abstract
The lowered self-esteem of First Nations people is evident in the disparities in health that exist in comparison with the rest of the Canadian population. High risk behaviors such as alcohol and drug use, and poor decisions relating to health and wellness are the outcome of decades of negative perceptions of self brought on by the lateral violence of colonialism. This research demonstrates how different determinants of First Nations identity (legal and policy based, social and culturally based definitions, and the self-identification ideology) interplay and influence a sense of authenticity which informs self-worth and the ability to realize health and wellness for twelve First Nations women on Manitoulin Island. First Nations identity is multi-layered and for women who only have one First Nations parent, and who often have Bill C-31 Indian status, identity becomes complicated and painful. Using life histories, the research participants demonstrate that an authentic identity is difficult to navigate because of the stigmatization they feel by non First Nations people for being a First Nations woman, and also the lateral violence they experience in their communities for being "bi-racial", not growing up on their reserve, not knowing language and culture, and often having either Bill C-31 Indian status or no status at all. The medicine wheel is used to explore this topic and a Nanabush story provides the context to understand it.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".