Bibliographic record
Abstract
“The world’s languages in crisis” (Krauss 1992), the great linguistic call to arms in the face of the looming language endangerment crisis, was first delivered in an Endangered Languages Symposium at the 1991 annual meeting of the Linguistic Society of America. Using the best available sources, he surveyed the global situation and estimated that only 10% of languages seem safe in the long term, up to 50% may already be moribund, and the remainder are in danger of becoming moribund by the end of this century. Twenty years later, better information is available. In this paper we use information from the latest edition of the Ethnologue (Lewis, Simons & Fennig 2013) to offer an update to the global statistics on language viability. Specifically the data for this study come from our work to estimate the level of every language on earth on the EGIDS or Expanded Graded Intergenerational Disruption Scale (Lewis & Simons 2010). Our finding is that at one extreme more than 75% of the languages that were in use in 1950 are now extinct or moribund in Australia, Canada, and the United States, but at the other extreme less than 10% of languages are extinct or moribund in sub-Saharan Africa. Overall we find that 19% of the world’s living languages are no longer being learned by children. We hypothesize that these radically different language endangerment outcomes in different parts of the world are explained by Mufwene’s (2002) observations concerning the effects of settlement colonization versus exploitation colonization on language ecologies. We also speculate that urbanization may have effects like settlement colonization and may thus pose the next great threat to minority languages.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".