Bibliographic record
Abstract
Kijiikwewin-aji means ‘to become a woman now’ in Algonquin and describes the heart of the research. Sweetgrass stories is part of the research methodology used with traditional Indigenous women. I formed an Indigenous research methodology called sweetgrass story weaving which focuses on traditional Indigenous women as they share their moontime stories. I also share information relating to the historical roots and present state of rites of passage with traditional Indigenous women. You will read traditional Indigenous women’s voices as they look back through lived experiences; hope and determination when looking forward to the future, and the shared theme of wanting their cultural traditions and ceremonies to live on through future generations of Indigenous girls and women, including young men. What is the current state of the Berry Fast, understanding the assimilative nature of colonization and the effects it has had on Indigenous women? How can we continue to honour these rites of passage while living in a world both with traditional Indigenous worldviews and colonial constructs? Over time, the collective strength and wisdom of traditional Indigenous women will increase which is a step in the decolonized direction of preventative health care which promotes mino bimaadiziwin.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".