Decomposing definiteness: Evidence from Chuj
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This article explores the realization of definiteness in Chuj, an underdocumented Mayan language. I show that Chuj provides support for recent theories that distinguish between weak and strong definite descriptions (e.g., Schwarz 2009, 2013; Arkoh and Matthewson 2013; Hanink 2018; Jenks 2018). A set of morphemes called “noun classifiers” contribute a uniqueness presupposition, composing directly with nominals to form weak definites. To form strong definites, I show that two pieces are required: (i) the noun classifier, which again contributes a uniqueness presupposition, and (ii) extra morphology that contributes an anaphoricity presupposition. Chuj strong definites thus provide explicit evidence for a decompositional account of weak and strong definites, as also advocated in Hanink 2018. I then extend this analysis to third person pronouns, which are realized in Chuj with bare classifiers, and which I propose come in two guises depending on their use. On the one hand, based on previous work (Postal 1966, Cooper 1979, Heim 1990), I argue that classifier pronouns can sometimes be E-type pronouns: weak definite determiners which combine with a covert index-introducing predicate. In such cases, classifier pronouns represent a strong definite description. On the other hand, I argue, based on diagnostics established in Bi and Jenks 2019, that Chuj classifier pronouns sometimes arise as a result of NP ellipsis (Elbourne 2001, 2005). In such cases, classifier pronouns reflect a weak definite description.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it