Bibliographic record
Abstract
In response to a crescendo of public and scholarly interest, over the last two decades there has been a noticeable and mostly welcome surge in publications that focus on language documentation, conservation, and revitalization. Early and high impact contributions in Hale et al. (1992) included a now seminal article by Michael Krauss which called for urgent action to prevent linguistics from going down in history as the ‘only science that presided obliviously over the disappearance of 90% of the very field to which it is dedicated’ (Krauss 1992:10). There then followed a discussion on the topic by Ladefoged (1992) and a prompt reply by Dorian (1993) that situated the issue of language endangerment as one deserving of sustained academic attention. Alongside swelling bookshelves that speak to the urgency of this work, major research programs funded by private philanthropic organizations and research councils were also being established at this time. The Foundation for Endangered Languages (FEL) was founded in 1995, followed a year later by the Endangered Language Fund (ELF). With the establishment of theDokumentation Bedrohter Sprachenprogram (DoBeS) in 2000, the Hans Rausing Endangered Languages Project (HRELP) in 2002, and the Documenting Endangered Languages (DEL) program funded by the US government in 2005, the last two decades bear witness to a steady increase in support, funding, and visibility for the documentation and preservation of endangered languages.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.229 | 0.123 |
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".