Bibliographic record
Abstract
This chapter offers practical guidance for funding language revitalization projects, particularly for language activists and community members. Some organizations devoted to endangered languages provide grants for revitalization efforts; funding may also be available from government bodies (mainly in the US and Canada) and other grants and scholarships. For smaller projects, crowd funding and other community initiatives have the additional benefits of raising awareness of the language revitalization program and involving the wider community. The factors that reviewers consider in assessing formal funding proposals are discussed, including the importance, feasibility and design of the project; the applicant’s connection to the community and ability to complete the work; and the appropriateness of the budget, including guidance on common budget categories and expenses. The capsule reports on a survey investigating attitudes of NGOs in Guatemala towards language revitalization; local organizations and institutions with a vested interest in local people were more likely to provide practical and financial support for revitalization initiatives.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.323 | 0.182 |
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".