Language revitalisation in a multilingual community: the case of Michif(s)
Bibliographic record
Abstract
In this talk, we discuss how the forcing of a Western model of identity where one culture = one language has caused divisiveness within the traditionally multilingual and multicultural Métis community, and discuss ways in which to overcome this divisiveness when attempting collaborative language revitalization initiatives. The Métis are the descendants of French and First Nations intermarriage in Canada’s Northwest during the Fur Trade. In the early 19th century, the Métis flourished and began to think of themselves as a separate people (Sealey & Lussier 1975: 3), distinct from either French or First Nations. Métis people traditionally spoke a few of Cree, Ojibwe, Sioux, French, English, and a Plains Cree-French mixed language, called Michif today. In this talk, we show that the multilingualism and multiculturalism of the Métis people which allowed them to flourish in the early 19th century may hold back progress in revitalization and documentation today, due to an attempt to fit a traditionally multilingual people into a Western tradition of unilingualism. For revitalization efforts to succeed, it is often said that the language community must actively support the language and the revitalization efforts. Delineating this community is not normally an issue; the language shares the same name as the ethnicity/culture, or community members feel bound together by their language. However, the Michif/Métis situation is much more complicated. The multilingual nature of the culture results in the term Michif designating at least 3 languages: the Métis variety of French; the Métis variety of Cree, and the Plains Cree-French mixed language. Although the Métis share history, music and traditions, language has become a catalyst for community divisiveness rather than unity, which becomes a serious problem in collaborative revitalization efforts. For example, in order to access federal funding, the mixed-language Michif has been designated as the Métis Nation’s official language, resulting in the hierarchicization of this language over the others. Speakers of this language are now the prestige group, eligible for funding, while speakers of other Métis languages are left frustrated that their language has been demoted by their own people, unable to access the same funding. This is just one example discussed of the difficulties arising from the shift from a multilingual to a unilingual society. This paper shows how an inclusive model such as Junker’s East Cree language project may be used to not only renew pride in a language, but also pride in a multilingual society.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".