‘Nehiyawewin Askîhk’: Cree Language on the Land: Language Planning Through Consultation in the Loon River Cree First Nation
Bibliographic record
Abstract
This article examines the parallel development of language planning and land planning within the Loon River Cree First Nation. Loon River Cree territory, located in north-central Alberta, Canada, is an area where major oil and gas industry, as well as logging and mining are constantly encroaching. The community, who still use Cree in their daily lives, completed a Traditional Land Use Study in 2004 which documented the historical and contemporary relationship the Loon River members have with their land. The study compiled oral histories from 20 elders, all in the Cree language, and also included site visits to important locations, digital mapping and archival research. The Traditional Land Use Study has since resulted in the creation of a Consultation Unit. The role of the Consultation Unit, which consists mostly of Loon River Cree community members, is to be an intermediary between industry, the provincial government of Alberta, and the First Nation. However, the Consultation Unit's goals also include, ‘Protect[ing] the culture, language, and lifestyle of the Loon River First Nation community and membership’ (Loon River Cree First Nation, 2006, Consultation Unit, Policies and Procedures, emphasis added). The increase of industry on Loon River Cree land will inevitably lead to an increase of English being spoken in their territory, and it is the lands and resources sector of their community that is assigned the significant task of protecting the Cree language and planning for the future.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".