Bibliographic record
Abstract
This article examines a puzzle pertaining to the distribution of covalued nominals in two understudied Mayan languages, Chuj and Ch’ol. While Ch’ol behaves as expected with regard to the binding conditions, Chuj appears to consistently tolerate violations of Condition C. The Chuj data thus cast doubt on the widely held view that the binding conditions reflect a universal property of human language (e.g., Grodzinsky and Reinhart 1993, Reuland 2011). I argue that the difference between Chuj and Ch’ol can be largely explained if, contrary to Ch’ol, Chuj exhibits “high-absolutive” syntax, independently proposed by Coon, Mateo Pedro, and Preminger (2014) to explain a constellation of morphosyntactic properties shared by a subset of Mayan languages. This syntax bleeds otherwise expected binding relations from the subject into the object, explaining the apparent violations. I further show that linear precedence plays a fundamental role in regulating the distribution of covalued nominals across Mayan, which I argue is due to a general ban on cataphoric “free” pronouns. Thus, the Mayan data not only are consistent with the binding conditions, but also provide further evidence in favor of a deep typological parameter within the Mayan language family.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".