Bibliographic record
Abstract
Indigenous languages are at risk of extinction in Canada, and also at risk are the traditional \nstorytelling ways of our ancestors. Our First Peoples have been using oral transmission to pass on cultural knowledge about our Indigenous ways of life from generation to generation since time immemorial. Storytelling is used to teach our young people about our beliefs, values, history and relationships. This project explores how one researcher’s personal journey utilized a storywork approach to connect to her cultural identity and language by telling four of her personal stories in Hul’q’umi’num’, a Coast Salish language of British Columbia. The stories and their English translations are given in the Appendix. The researcher is not yet a speaker of her language, but she proceeded with the support and guidance of a collaborative team of Quw’utsun’ Elders and language specialists. This report details the step-by-step learning process that a person can undertake to construct stories even if they are not fluent speakers of a language. The researcher learned much about the sounds and structures of her language as well as how new stories are designed. Through this process, the research was able to share teachings, important messages, traditional knowledge and a Quw’utsun’ worldview in her own language. By telling her own stories and making them available to her community in the form of texts and movies, this project makes a contribution to the Hul’q’umi’num’ language revitalization strategy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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; both teacher heads agree on what is shown here.
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".