Supporting small languages together: The history and impact of the International Conference on Language Documentation & Conservation series
Bibliographic record
Abstract
The International Conference on Language Documentation & Conservation series, or ICLDC, has, since its inception in 2009, become the flagship conference for the field of language documentation. Every two years, conference attendees gather at the University of Hawai‘i at Mānoa to share their experiences working on diverse topics related to the preservation of underrepresented languages worldwide. Attendees come from a range of backgrounds: Indigenous language communities, language activism organizations, K–12 school systems, as well as students and faculty from colleges and universities. They represent dozens of countries and hundreds of languages, and they have one goal in mind: supporting small languages together. In this paper, we trace the history of the ICLDC series since the first iteration and discuss the scope of its impact on the field of language documentation and conservation according to conference attendees. We also look ahead to the changes that the covid-19 pandemic will bring to the structure of the conference in 2021 and beyond.
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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 | 0.028 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".