<scp>Fèlix Martí, Paul Ortega, Itziar Idiazabal, Andoni Barreña, Patxi Juaristi, Carme Junyent, Belen Uranga and Estibaliz Amorrortu</scp> (eds.), <i>Words and worlds: World languages review</i>
Bibliographic record
Abstract
Fèlix Martí, Paul Ortega, Itziar Idiazabal, Andoni Barreña, Patxi Juaristi, Carme Junyent, Belen Uranga and Estibaliz Amorrortu (eds.), Words and worlds: World languages review . Clevedon, Buffalo, & Toronto: UNESCO Etxea and Multilingual Matters, 2005. Pp. xv, 328. Hb. $89.95 This volume reports on the World Languages Review Project, financed by the Basque Government in cooperation with UNESCO. The project's technical committee members have coauthored much of the book, with highlighted contributions from experts from around the globe. Content is based on responses to open questionnaires, distributed through the project committees' networks to individuals knowledgeable on the linguistic situation of each of the 525 languages considered. Other data come from continental meetings, expert consultations, publications, catalogues, atlases, and language centers. This information is analyzed in 12 chapters describing aspects of diversity, indices of vitality and/or decay, and key domains for language maintenance. Chapters conclude with recommendations for the development of language policy. In these ways, the authors achieve their goal of increasing awareness and “appeal[ing] to the responsibility of everyone” (p. xii) to protect languages around the world.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.094 | 0.056 |
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".