Strengthening Relationships Between Indigenous Communities, Documentary Linguists, and Computational Linguists in the Era of NLP-Assisted Language Revitalization
Bibliographic record
Abstract
As the global crisis of language endangerment deepens, Indigenous communities have continued to seek new means of preserving, promoting and passing on their languages to future generations.For many communities, modern language technology holds the promise of accelerating that process.However, the cultural and disciplinary divides between documentary linguists, computational linguists and Indigenous communities have posed an on-going challenge for the development and deployment of NLP applications that can support the documentation and revitalization of Indigenous languages.In this paper, we discuss the main barriers to collaboration that these groups have encountered, as well as some notable initiatives in recent years to bring the groups closer together.We follow this with specific recommendations to build upon those efforts, calling for increased opportunities for awareness-building and skills-training in computational linguistics, tailored to the specific needs of both documentary linguists and Indigenous community members.We see this as an essential step as we move forward into an era of NLP-assisted language revitalization.
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.016 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.004 | 0.007 |
| 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".