Random Choice from Likelihood: The Case of Chuj (Mayan)
Bibliographic record
Abstract
Abstract Research on modality has recently broadened beyond the verbal domain, unearthing questions about the cross-categorial nature of modality ( Arregui et al. 2017), for instance: To what extent do DP and VP modals mirror each other? Chuj, an understudied Mayan language, provides an ideal vantage point to answer this question with respect to random choice modality. Random choice indefinites convey, roughly, that an agent made an indiscriminate choice. In Chuj, random choice indefinite DPs involve a morpheme (komon) that can also appear as a verbal modifier (Royer & Alonso-Ovalle 2019), inviting a comparison between categories. We argue that both in DPs and VPs, komon conveys information about the likelihood of the event described, but that the modal component of komon is nevertheless tied to its syntactic position. VP-komon conveys that the most expected worlds where the described event happens are no more expected than the most expected worlds where it does not. DP-komon conveys a similar modal component, but hardwires a comparison between the likelihood of the event described, which involves an individual in the extension of the NP, and that of alternative events determined by considering alternative individuals in the extension of that NP. The characterization of the modal component of komon contributes to the characterization of random choice modality and brings into question whether this type of modality should be taken to be a unified category, since none of the previous proposals on the nature of random choice modality tie it to the expression of likelihood.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".