Using questionnaires as a tool for comparative linguistic field research: Two case studies on Javanese
Bibliographic record
Abstract
In this paper, we discuss how written questionnaires for targeted constructions can be a beneficial tool for comparative linguistic field research through two case studies on Javanese (Austronesian; Indonesia). The first case study is based on a questionnaire designed to elicit how a language or a dialect expresses the semantic meaning of modality (Vander Klok 2014); we show how it can be implemented in three different ways for comparative linguistic field research. The second case study is based on a questionnaire which investigates the morphosyntax of polar questions across four Javanese dialects; we show how items can be designed to maximize direct comparison of features while still allowing for possible lexical, phonological, or morphosyntactic variation. Based on these two studies, we also address methodological challenges that arise in using questionnaires in comparative linguistic field research and offer best practices to overcome these challenges.
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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 | 0.035 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".