Joshua A. Fishman (ed.), <i>Can threatened languages be saved? Reversing language shift, revisited: A 21</i><sup><i>st</i></sup><i> century perspective</i>. Clevedon: Multilingual Matters, 2001. Pp. xvi, 503. Pb $24.95.
Bibliographic record
Abstract
This volume revisits, as its title states, the theory and practice of reversing language shift (RLS) first proposed by Fishman in 1991. A dozen of the original case studies are reanalyzed and several more are added, producing a rich source of detail on some of the specific situations of language shift and efforts to reverse it. Fishman contributes introductory and concluding chapters as well as one of the case studies (Yiddish); other authors cover Navajo, New York Puerto Rican Spanish, Québec French, Otomí, Quechua, Irish, Frisian, Basque, Catalán, Oko, Andamanese, Ainu, Hebrew, immigrant languages in Australia, indigenous languages in Australia, and Maori. The resulting book provides a wealth of information about language shift and public policy directed toward RLS, but its aims are broader than that.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".