Can threatened languages be saved? : reversing language shift, revisited : a 21st century perspective
Bibliographic record
Abstract
PREFACE: Reversing Language Shift 1. Why is it so hard to save a threatened language? Joshua A. Fishman THE AMERICAS: 2. Reversing Navajo Language Shift, Revisited Tiffany Lee (Stanford Univ) & Daniel McLaughlin (Dine College) 3. How Threatened is the Spanish of New York Puerto Ricans? Ofelia Garcia (Long Island Univ) Jose Luis Morin (City Univ of New York) & Klaudia Rivera (Long Island Univ) 4. A Decade in the Life of a Two-in-One Language - Yiddish in New York City Joshua A. Fishman 5. Reversing Language Shift in Quebec Richard Y. Bourhis (Universite du Quebec a Montreal) 6. Otomi language shift and some recent efforts to reverse it Yolanda Lastra (Universidad Nacional Autonoma de Mexico) 7. Reversing Quechua language shift in South America Nancy H. Hornberger (Univ of Pennsylvania) & Kendall A. King (New York Univ). EUROPE: 8. Irish Language Production and Reproduction 1981-1996 Padraig O Riagain (Institiuid Teangeolaiochta Eireann) 9. A Frisian Update of Reversing Language Shift Durk Gorter (Fryske Academy) 10. Reversing Language Shift: The Case of Basque Maria-Jose Azurmendi (Univ of the Basque Country), Erramun Bachoc (Basque Cultural Institute), Francisca Zabeleta (Public University of Navarre) 11. Catalan A Decade Later Miquel Strubell (Universitat Oberta de Catalunya) . AFRICA AND ASIA: 12. Saving Threatened Languages in Africa: A Case Study of Oko Efurosibina Adegbija (Univ of Ilorin, Nigeria) 13. Andamanese: Biological Challenge for Language Reversal E. Annamalai & V. Gnanasundaram (C.I.I.L, Mysore) 14. Akor Itak Our Language, Your Language - Ainu in Japan John C. Maher (International Christian Univ, Tokyo) 15. Hebrew After a Century of RLS Efforts Bernard Spolsky (Bar-Illan Univy) & Elana Shohamy (Tel Aviv Univ). THE PACIFIC: 16. Can the Shift from Immigrant Languages be Reversed in Australia? Michael Clyne (Monash Univ) 17. Is the Extinction of Australia's Indigenous Languages Inevitable? Joseph Lo Bianco & Mari Rhydwen (National Language and Literacy Institute of Australia) 18: RLS in Aotearoa/New Zealand 1989-1999 Richard & Nena Benton (Waikato University). CONCLUSIONS: 19: From Theory to Practice (and Vice Versa): Review, Reconsideration and Reiteration Joshua A. Fishman
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".