Bibliographic record
Abstract
Many Indigenous languages in Canada are facing the threat of extinction. While some languages remain in good health, others have already been lost completely. Immediate action must be taken to prevent further language loss. Throughout Canada’s unacceptable history of expunging First Nations’ ways of life, systemic methods such as residential schools attempted to eradicate Indigenous cultures and languages. These efforts were not entirely successful but Indigenous language and culture suffered greatly. For Indigenous communities, language loss impaired intergenerational knowledge transfer and compromised their personal identity. Additionally, the cumulative effects of assimilation have contributed to poor mental and physical health outcomes amongst Indigenous people. However, language reclamation has been found to improve well-being and sense of community. To this objective, this paper explores the historical context of this dilemma, the lasting effects of assimilation, and how this damage can be remediated. Additionally, we examine existing Indigenous language programs in Canada and the barriers that inhibit the programs’ widespread success. Through careful analysis, such barriers may be overcome to improve the efficacy of the programs. Institutions must quickly implement positive changes to preserve Indigenous languages as fluent populations are rapidly disappearing.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".