Indigenous Languages: Their Threatened Extinction Is a Global Responsibility
Bibliographic record
Abstract
Abstract Indigenous languages are at the verge of extinction. For many indigenous communities, saving their languages means protecting one of the last-standing symbols of their cultural identity, a symbol that has survived a history of colonization and that can impact their well-being. If indigenous languages are to survive, language revitalization strategies need to be adopted by indigenous communities and governments. One such strategy is language revitalization planning, where communities and governments are actively engaged in changing the way group members use language. Language revitalization plans are often derived from two theoretical stands, either language reversal theory (which adopts a language-autonomy perspective) or language vitality (which focuses on the factors that favor a linguistic group’s survival). Language revitalization strategies also involve some form of bilingual education. Bilingual education in indigenous communities allows indigenous children to learn, and hence to gain competency in, both their indigenous language and the mainstream language. Strong forms of bilingualism, as opposed to weak forms of bilingualism, have great potential for nourishing competency in indigenous languages, because they give equal value to the indigenous language and the mainstream language. Language revitalization strategies also need to consider the collective functions of language, or how groups use their language. Language can be used by groups as a vehicle for cultural knowledge, as a symbol of identity, and as a tool for communicating in formal and informal settings. Strengthening the collective function of indigenous languages is essential to their survival. In the case of indigenous people, every single step taken to revitalize their languages (language planning, bilingual education, and the collective functions of language) is an affirmation of their continuous existence in the world, upholding their distinctiveness from colonizers. This “collective existential affirmation” of indigenous people may very well be the drive needed to achieve language revival.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".