Tongues on the EDGE: language preservation priorities based on threat and lexical distinctiveness
Bibliographic record
Abstract
Languages are being lost at rates exceeding the global loss of biodiversity. With the extinction of a language we lose irreplaceable dimensions of culture and the insight it provides on human history and the evolution of linguistic diversity. When setting conservation goals, biologists give higher priority to species likely to go extinct. Recent methods now integrate information on species evolutionary relationships to prioritize the conservation of those with a few close relatives. Advances in the construction of language trees allow us to use these methods to develop language preservation priorities that minimize loss of linguistic diversity. The evolutionarily distinct and globally endangered (EDGE) metric, used in conservation biology, accounts for a species' originality (evolutionary distinctiveness-ED) and its likelihood of extinction (global endangerment-GE). Here, we use a similar framework to inform priorities for language preservation by generating rankings for 350 Austronesian languages. Kavalan, Tanibili, Waropen and Sengseng obtained the highest EDGE scores, while Xârâcùù (Canala), Nengone and Palauan are among the most linguistically distinct, but are not currently threatened. We further provide a way of dealing with incomplete trees, a common issue for both species and language trees.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".