Bibliographic record
Abstract
Working with speakers of endangered languages often involves developing a deep rapport with the eldest members of a community. These relationships present unique challenges that include navigating great losses – not only of the language of study, but, more profoundly, the attendant death of its speakers. This essay is motivated by the recognition that the death of close consultants is inherent in work with endangered languages. It draws on case study examples to examine the emotional components of language work, specifically grief and loss, from both personal and professional perspectives. Our focus is on two key issues. The first is as a methodological issue that arises for those operating under a collaborative model of language work where investment by the community and participatory research by the fieldworker is the norm. The second is as a training issue involving our responsibilities to those we mentor in understanding the reality of close work with speakers, particularly of endangered languages. This reality includes careful consideration of their families and communities. Our hope is that this essay may serve as a foundation upon which a more thorough consideration of methodological issues and preparation through honest and open approaches to training can be constructed.
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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.029 | 0.078 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".