Nuk’wantwal’: Collaborative and Community-Centred Approaches to Language Vitalization from an Indigenous Perspective
Bibliographic record
Abstract
The people working to keep their languages thriving need the help, wisdom, support and expertise of a broad range of communities – government, organizations, institutions, citizens and funders – as well as the expertise and wisdom found in their own language communities and organizations. This paper will describe the collaborative relationships built between language communities and governments, universities, institutions, and provincial organizations to work together to support language vitalization in British Columbia. The challenges and opportunities when working with a diverse set of languages and dialects and a broad range of levels of language vitality will be highlighted. \n \nCeux et celles qui travaillent pour conserver la prospérité de leurs langues ont besoin de l’aide, de la sagesse, du soutien et de l’expertise d’un éventail de communautés: gouvernement, organismes, institutions et citoyens, entre autres. Il leur faut des sources de financement ainsi que l’expertise et la sagesse qui se trouvent dans leurs propres organismes et communautés linguistiques. Cet article décrit les relations de collaboration établies entre des communautés linguistiques et les gouvernements, les universités, les institutions et les organismes provinciaux pour travailler ensemble afin d’appuyer la vitalité linguistique en Colombie-Britannique. Nous soulignerons les défis et les opportunités qui se présentent dans le travail avec divers langues et dialectes et différents niveaux \nde vitalité linguistique.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.036 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| 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".