ᐧᐄᒑᐦᑐᐧᐃᓐᐦ, ᐊᔨᒥᐧᐃᓐ, ᑭᔮ ᐊᔅᒌ: ᐋᐧᐄ ᔮᔨᒋᑳᐱᐧᐃᐦᑖᑭᓂᐧᐃᒡ ᐊᔨᒥᐧᐃᓐ ᐊᓂᑖ ᐧᐄᒥᓂᒌᐦᒡ \nRelationships, Language, and the Land: Language Revitalisation in the Cree Community \nof Wemindji, Eeyou Istchee
Bibliographic record
Abstract
Indigenous languages, lands, and cultures are inextricably linked, and language is critical for cultural retention and transmission, individual and community well-being, and identity. While Indigenous languages worldwide risk being lost, language activists are emerging from communities to protect their ancestral languages, heritages, and connections to land. In Canada, approximately 70 Indigenous languages are spoken today; however, the Cree dialect continuum is one of only three expected to endure. The legacy of Canadian residential schools and other colonial practices have had lasting impacts on the relationships to language and land \nof many Eeyouch (Eastern Cree people). In response, the Eeyou (Eastern Cree) community of Wemindji launched the Cree Literacy for Wemindji Adults program (CLWA) in 2017. In this manuscript-based master’s thesis, undertaken at the invitation of the community and Community Council, I explore the intimate relationships between iiyiyuuayimuwin (Eastern Cree language) and ischii, and the implications of language reclamation for miyupimaatisiiun (Eeyou community and individual well-being), as shared with me by community members. In the first of two manuscripts, I demonstrate how dispossession caused by colonial encroachment and neocolonial extractivism has caused these relationships to weaken, and explore community responses to these impacts over several generations. In my second manuscript, co-author and Wemindji Language coordinator and Cree language teacher, Theresa Kakabat-Georgekish and I explore the impacts of the process of language reclamation on CLWA participants’ and \ncommunity well-being and sense of cultural identity.
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 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 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".