Corpus linguistics and language documentation: challenges for collaboration
Bibliographic record
Abstract
Recent literature in corpus linguistics (e.g., McEnery & Ostler 2000) and language documentation (e.g., Johnson 2004) suggests both disciplines may share natural points of interaction, having in common an interest in the construction and use of permanent collections of diverse linguistic data. Although considerable benefit might be anticipated from close collaboration between these two areas, divergences in their respective purposes, practices, and products may render such an interaction more difficult to foster than might initially be expected. This paper considers points of commonality and difference between corpus linguistics and language documentation in four specific areas of practice, drawing upon examples from ongoing corpus construction and language documentation efforts centered on Mennonite Plautdietsch in Canada. Given the results of this comparison, this study proposes viewing corpora as descriptive applications of language documentation, to be built directly upon the permanent documentary record. By founding corpora upon documentary materials, such an approach opens language documentation more readily to the analytical and methodological contributions of corpus linguistics, while providing a solid empirical basis for future corpus construction.
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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 | 0.102 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.040 | 0.061 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".