Environmental Change and Sustainability of Indigenous Languages in Northern Alaska
Bibliographic record
Abstract
Relatively few people under the age of 60 are fluent speakers of the various Indigenous languages of Alaska. Concurrently, climate change is severely impacting Alaska and its residents, where environments are changing far more rapidly than the majority of the planet. These factors complicate the land-language nexus and may have implications for the sustainability of Indigenous languages in Alaska and other parts of the Arctic. In this collaborative, community-centered project, we spoke with Iñupiaq and Yupik language speakers to learn how rapid environmental change affects heritage language discourse practices and how generational gaps in levels of heritage language fluency affect safety and efficacy of customary and traditional land use activities. The results show how local community choices and attitudes are reflecting and constructing dynamic ecologies of language, culture, and environment. Iñupiaq and Yupik languages provide important forms of socio-cultural resilience because they embed the past, yet are inherently dynamic. Community-driven social practices that promote increased local heritage language use can lead to new, creative language domains, new expressions of Indigenous culture, and new Indigenous stances toward a changing environment.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".