Bibliographic record
Abstract
Wikipetcia Atikamekw Nehiromowin is the Wikipedia version in the Amerindian language Atikamekw, spoken by the First Nation of the same name in Quebec, Canada.Of the 8,000 members of the Atikamekw First Nation, 97.9% speak their native language at home (INAC 2020).In 2013, the linguist Nastasia Herold did a field study in Manawan, one of the three Atikamekw communities, for research on local bilingualism (Atikamekw and French).Despite the vitality of the Atikamekw language, a survey and interviews showed that francization and language change are processes noticed by all living generations of the Atikamekw (Herold 2021).Today, communication takes place more and more often digitally (Reichert 2017), in written, rather than oral, modes.Atikamekw, traditionally an exclusively oral language, has had a standardized orthography since 1994 (Dinnison 1997) and is taught in Manawan's primary school as a first language and a medium of alphabetization.However, Herold's (2019) research in 2013 showed that the Internet contained no written text in the Atikamekw language, and the Atikamekw used the Internet mainly in French.This is why a school project at Manawan's secondary school was initiated in 2013 in order to create a Wikipedia site in the Atikamekw language.In their computer science lessons, the pupils of 10th and 11th grade learned how to write in wiki code, Wikipedia's markup language.They also learned about rules from the Wikimedia Foundation they have to consider as well as the rules of Wikipetcia itself (e.g., oral sources are allowed, following the local tradition, while articles about medical use of plants and secret ceremonies are forbidden).In order to give students help with writing in their native language, local elders and language specialists were at their sides as they created articles about flora, fauna, important places of their territory, Atikamekw personalities, traditional activities and more.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.109 | 0.034 |
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".