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Record W2727644564

Re Tlli7sa ell re uqw7úqwis: Engaging Indigenous language learners with an epic story through a language learning app.

2017· article· en· W2727644564 on OpenAlexaboutno aff
Ronald Ignace, Marianne Ignace

Bibliographic record

VenueAmericanae (AECID Library) · 2017
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousLinguisticsStorytellingEthnographyVocabularyGrammarNarrativeCONTESTComputer scienceHistorySociologyAnthropologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

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).

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0270.006

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.

Opus teacher head0.032
GPT teacher head0.351
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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