Stories with deep roots: Cultivating community–university relationships to facilitate the creation of Gwa’sala-’Nakwaxda’xw children’s stories
Bibliographic record
Abstract
Bak'wamk'ala is a language spoken on north-eastern Vancouver Island and on the islands and along coastal waterways nearby. One of the joys of life in these territories is the abundance of delicious berries that ripen throughout the summer: ťsagał (‘thimbleberries’), k'amdzakw (‘salmonberries’), gwadam (‘huckleberries’), ʼnak'wał (‘salalberries’) and more. Kwakwaka'wakw culture includes a long tradition of knowledge and technologies related to berry-picking: special baskets, protocols for picking, songs and stories. Children accompany their parents while berry-picking as babies in carriers and gradually walking alongside with their own small baskets; for this reason, berry-picking is an especially suitable topic for a children’s book seeking to highlight and foreground Kwakwaka'wakw culture for Kwakwaka'wakw children (and others). We share here the text of a children’s book about picking thimbleberries by Lucy Hemphill (Gwa’sala-’Nakwaxda’xw), and a reflection written by Ms Hemphill and Daisy Rosenblum, a professor in the First Nations and Endangered Languages Program at the University of British Columbia, which describes the iterative process of including Bak'wamk'ala in the English version of the story, and planning for the Bak'wamk'ala language version of the book. Through our reflection, we discuss the choice of the story’s theme, the value of written resources created for languages with previously oral traditions and the challenges inherent in such processes of creation.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".