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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.015 |
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".