Bibliographic record
Abstract
This article presents a sociolinguistic characterization of the indigenous Métis people in Canada. Drawing on existing scholarly works and research, the study aims to describe the status of the Métis people, recently recognized as an indigenous nation of the country, and their language. It delves into the historical formation of the Métis nation and its current state, providing a demolinguistic profile of the Métis population today. Statistical data from recent censuses is included. The novelty of this research lies in its examination of the contemporary situation of the Métis people with a focus on existing laws and judicial decisions impacting all aspects of their lives. An overview of anthropological and sociolinguistic studies on Métis people conducted by scholars over the past decades is offered. The author emphasizes the functional characteristics of the Métis language, Michif, outlining its ethnic and social functions. The article also discusses policies concerning Métis people in the realms of social, economic, and political rights. The relevance of this study is underscored by the heightened attention from Russian and global societies towards language situations and solutions to linguistic issues in polyethnic states, as well as the preservation of indigenous languages.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".