Bibliographic record
Abstract
British Columbia, Canada, is rich with a great diversity of First Nations languages and cultures. B.C. is home to 60% of First Nations languages in Canada with 34 unique languages. In 2010, we took a close look at this diversity with the publication of our Report on the Status of B.C. First Nations Languages. Based on input provided by First Nations communities, the report outlined a detailed picture of the language situation in the province with regard to numbers of speakers, semi-speakers, learners and language resources. For the first time, we were able to present an accurate snapshot of the state of B.C.’s First Nations languages, with the goal of providing useful information for First Nations leadership, governments, communities and language stakeholders to use in revitalization efforts at all levels. We aimed to provide evidence for the urgency to act, and give direction on successful language revitalization strategies to inspire action. We believe that progress was made toward achieving our stated goals; the release of the report resulted in thousands of citations in the media, increased financial support and more attention from the general public. Four years later, we are very pleased to present the second edition of the Report on the Status of B.C. First Nations Languages. This report presents the current status of B.C. First Nations languages and highlights examples of successful community-based language revitalization strategies. The 2014 statistics show that while progress is being made in terms of increased semi-speakers, much more work needs to be done while fluent speakers are still with us. First Nations leadership, community members, all levels of government and the general public all have a role to play in language revitalization efforts.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".