How to investigate linguistic diversity: Lessons from the Pacific Northwest
Bibliographic record
Abstract
On the basis of five case studies from languages of the American Pacific Northwest, we argue that, at least in the areas of syntax and semantics, a scientific approach to the study of linguistic diversity must be empirically grounded in theoretically informed, hypothesis-driven fieldwork on individual languages. This runs counter to recent high-profile claims that large-scale typology based on the sampling of descriptive grammars yields superior results. We show that only a hypothesis-driven approach makes falsifiable predictions, and only a methodology that yields negative as well as positive evidence can effectively test those predictions. Targeted elicitation is particularly important for languages with a small number of speakers, where statistical analysis of large-scale corpora is impossible. Given that a large proportion of the world’s linguistic diversity is found in such languages, we conclude that formal, hypothesis-driven fieldwork constitutes the best way rapidly and efficiently to document the world’s remaining syntactic and semantic diversity.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.004 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".