Re Tlli7sa ell re uqw7úqwis: Engaging Indigenous language learners with an epic story through a language learning app.
Bibliographic record
Abstract
While some Indigenous languages in northwestern North America during the late 1800s and early 1900s fortuitously had local people, linguists and ethnographers recording verbatim texts from indigenous storytellers, Interior Salish languages for the most part did not benefit from that legacy, although in the 1960s to 1990s some text recording work – by then in audio – was done by linguists and ethnographers. However, as it pertains to the Secwepemc (Shuswap) language, in good part we are left with a body of English-only stsptekwll or oral traditions (Dawson1891, Boas 1895, Teit 1900) that were not recorded in the Indigenous language but were written down in English, at times with great detail, at times in summary form. Working in and through Secwepemctsin, between 2014 and 2016 the authors reconstructed the lengthy transformer epic of Tlli7sa and his brothers with a group of fluent speakers/elders. This stsptekwll involved (re)-constructing a detailed text of more than 300 sentences. Paying close attention to authenticity and detail in vocabulary as we described actions and movement, clothing and implements, and to ecology and geography, we produced detailed and, as best as we could, accurate narrations of the eighteen episodes of the story. In addition, throughout the editing process, we paid close attention to Secwepemctsin discursive conventions and grammar constructions, e.g. topic tracking, subordination, the use of passive voice, and other stylistic means deployed in storytelling. In this process we re-claimed this complex epic through many rounds of collaborative story writing and telling, being mindful that our language consultants were all victims of Indian Residential schooling in Canada, thus themselves reclaiming story telling in its method, language and rhetorical devices. Finally, we connected the story to places in the landscape that had been almost forgotten by recent generations, and embarked on journeys to those places with the team of elders and learners. Adding illustrations co-produced by the elders and a graphic artist brought the stories to life, as did visiting the places where events took place. Finally, we speak to the successes and challenges of turning the Tlli7sa epic – and potentially future stories – into digital learning apps: Apps do not directly engage being on the land, but our experiences with connecting apps to lived experience and told knowledge, thus using them as tools, address the fact that they can produce consciousness of history and ancient landscape, and thus enhance a sense of collective and individual well-being on ancestral lands. References: Boas, Franz, 1895 (2016), Indianische Sagen von der Nord-Pazifischen Kuste Amerikas. Berlin. (Engl. Translation edited by R. Bouchard and D. Kennedy 2002) Dawson, George M., 1891, Notes on the Shuswap People of British Columbia. Transactions of the Royal Society of Canada. 1st Series 9(2):3-44. Teit, James A., 1909, The Shuswap. Memoirs of the American Museum of Natural History 4 (7); Publications of the Jesup North Pacific Expedition, 2 (7). Leiden and New York (Reprinted by AMS Press, New York, 1975).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".