Place Names Documentation as Community-Based Language Conservation
Bibliographic record
Abstract
Bristol Bay Native Corporation’s (BBNC) Bristol Bay Native Place Names Project is a 20-year initiative dedicated to celebrating the Sugpiaq/Alutiiq, Dena’ina, and Yup’ik Native place names in the Bristol Bay region of Southwest Alaska. Through this project, BBNC is committed to honoring the land-centered knowledge and environment that has long defined Alaska Native cultures. As traditional place names are increasingly replaced by English equivalents, this project serves to revitalize cultural and linguistic knowledge that connects our communities to the land. The Bristol Bay Native Place Names Project highlights the contributions of Alaska Native cultural workers, educators, community members, and knowledge bearers. It documents approximately 1,500 place names across three Alaska Native languages in the Bristol Bay region. The online publication of these names has received a remarkable response from the community, with growing use of traditional place names in daily life, navigation, search-and-rescue operations, oral histories, and land-based language education in schools. Overall, this initiative represents a form of Indigenous language revitalization driven by Alaska Native people for the benefit of their home communities, ensuring the continued connection to cultural heritage and fostering future generations’ understanding of their ancestral lands.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.006 |
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".