Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 the Dokumentation Bedrohter Sprachen program (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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it