The Austronesian and the Micronesian Comparative Dictionaries as CLDF datasets
Bibliographic record
Abstract
The Austronesian Comparative Dictionary has served as an important resource for the comparative study of Austronesian languages since Robert Blust started its compilation in 1990. Likewise, the Micronesian Comparative Dictionary - an online database of Proto-Micronesian Reconstructions previously published in Oceanic Linguistics by Byron Bender and colleagues - is an important reference point for comparative Linguistics. The legacy, online versions of both dictionaries share an uncertain future, and both have not been available in a structured format, amenable to quantitative methods. Thus, to preserve the content of both dictionaries for the scientific record and to increase interoperability of the data, we undertook a conversion of the dictionaries to CLDF datasets. While programmatic access to the data within each dictionary already provides a new level of usability, the true potential of data in CLDF lies in interoperability across datasets. This is particularly useful for the two dictionaries presented here, because Micronesian languages belong to the Austronesian family and so the Micronesian data could potentially complement the Austronesian Comparative Dictionary. With the CLDF datasets we lay the groundwork for tackling this challenge.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".