The Dichotomy of Auxiliaries in Javanese: Evidence from Two Dialects
Bibliographic record
Abstract
Cole et al. find that auxiliary fronting in yes–no questions divides auxiliaries in the dialect of Peranakan Javanese as spoken in Semarang, Central Java, Indonesia into two types: low auxiliaries that can front and high auxiliaries that cannot. In my research on two geographically distinct dialects of Javanese (Paciran Javanese, spoken in Paciran, East Java; and Standard Javanese, spoken in Yogyakarta and Solo, Central Java), I broach the question of whether this division is a feature of only Peranakan Javanese, given the uniqueness of this dialect. I present results from two different methods (elicitation and a Likert-type rating task via a questionnaire), showing that this division holds for auxiliary fronting in both Paciran Javanese and Standard Javanese. These results suggest that the division of auxiliaries is a property that holds across all dialects of Javanese. In further exploration of the structural properties of the two types of auxiliaries in Javanese, I show that two other syntactic constructions—VP-topicalization and Subject–auxiliary answers to yes–no questions—also exhibit the same dichotomy of auxiliaries in both dialects, where only low auxiliaries are grammatical. I offer significant amendments to Cole et al.'s analysis of auxiliary fronting in Javanese to account for these additional constructions.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".