Language Revitalization and its Discontents: An essay and review of Saving languages: An introduction to language revitalization
Bibliographic record
Abstract
Endangered languages are of obvious interest to linguists, and it is remarkable that recent levels of concern have shown that many of them are more or less committed to doing something to stem linguistic decline. This is a change from earlier hands-off postures that traditionally held it to be neither appropriate nor feasible to intervene in the social life of language; the work of academies, for example, was regularly interpreted as psychologically understandable but linguistically naive. The older view remains, however, more correct than many modern commentators would have us believe. This is because the newer “ecological” stance generally persists in discussing language as if it were almost a freestanding matter that could and would respond to focused intervention. This is plainly not the case. Wholesale social reworking is too revolutionary for modern ecolinguists; rather, only some selected adjustments are wanted— but this has generally proved unworkable. To intervene on behalf of a threatened minority language, for instance, while leaving intact all the other aspects of social evolution that link the community in desired and desirable ways with the wider world, has generally resulted in failure. The ecology of language is now very much a growth industry, but it is hard to see that it has done anyone any good—except, of course, for those scholars who have found ample opportunity for publishing arguments on the side of the angels, and for fostering debate, if only amongst themselves. The latter outcome is of course a common one across all sorts of scholarly discourse, but there is surely a special poignancy here, inasmuch as virtually all the writing is
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".