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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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".