Language endangerment in Vanuatu: Bislama likely does pose a threat in the world’s most language-diverse country
Bibliographic record
Abstract
Abstract A quarter of a century has passed since the late Terry Crowley summed up the academic consensus that the creole language Bislama poses no immediate threat to any of Vanuatu’s Indigenous languages. Does this remain true today? Since that time, evidence both quantitative and qualitative has been accumulating that indicates Bislama is indeed gaining ground at the expense of Vanuatu’s Indigenous languages. In a targeted review of this evidence, this article brings together and assesses the insights of linguists, ethnographers, and others on the causal mechanisms that explain an ongoing shift towards Bislama. It thereby provides both a critical analysis of the current state of knowledge and a provisional causal framework for understanding current threats, predicting future threats and, potentially, intervening to prevent future threats. Language endangerment and extinction is a global issue with devastating implications for communities worldwide—one which Vanuatu, the world’s most linguistically diverse country, has thus far remained resilient in the face of. However, this review of the available evidence warns that Bislama will continue to disrupt intergenerational transmission of Indigenous languages unless a suite of inter-related mechanisms can be counteracted.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".