Resposta dos Autores: OS ACERVOS E A DOCUMENTAÇÃO LINGUÍSTICA
Bibliographic record
Abstract
As more and more of the world’s languages become endangered, their documentation provides key resources for linguists and communities. Documentary linguists look to digital archives as an essential resource for ensuring the preservation, conservation, and access of the outcomes of their work. In this article, we consider the benefits and challenges associated with archiving in language documentation, relating to issues of preservation, conservation, access, ownership, and use of materials. We draw on our accumulated knowledge as scholars who are deeply involved in administering, contributing to, and using language archives, particularly relating to the indigenous languages of Latin America. We focus in particular on the relevance of language archiving in Brazil, and its significance for scholars, community members, and other stakeholders. Our discussion considers the steps that are needed to ensure the quality and longevity of resources; the principles and strategies by which archived materials may be made available; and ways in which language archives can inform ongoing work with indigenous languages. As we lay out here, language archives provide key resources for scholars and for communities who wish to revitalize, maintain, or simply remember their linguistic and cultural heritage.
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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 | 0.025 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".