wakká:raien – i have a story : mixed-blood Indigenous women, identity, and urban spaces
Bibliographic record
Abstract
This study explores the stories of three mixed-blood Indigenous women in relation to how they navigate their identity and connection to land in urban spaces. The women are between the ages of 19 and 30 and live on the traditional and ancestral territories of the Coast Salish people, also known as Vancouver, Canada. The women engage with Photovoice, a grassroots methodology that employs photography and storytelling, to share aspects of their life and their experiences as mixed-blood Indigenous women. Through this research study, I aim to create an opportunity for the women to share and to be heard. Through their personal narratives, I will help shed light on the realities of female Indigenous identities with the intention of disrupting whitestream discourses and perceptions of indigeneity within urban settings. I employ an epistemology entitled “Three Directions”, as well as draw on Indigenous Feminism with the aim of honouring the women’s voices. The sharing that transpired within this research speaks to the ongoing struggle for mixed-blood Indigenous women to fully embrace their roots due to the impacts of colonization and assimilationist policies, but it also speaks to their own resiliency in finding supports within the urban community and in drawing strength from the land.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".