Mayan animacy hierarchy effects and the dynamics of Agree
Bibliographic record
Abstract
Abstract In many Mayan languages, combinations of subjects and objects are restricted by relative animacy hierarchy effects: subjects must be at least as high as objects in terms of animacy. Building empirically on a novel description of Chuj, as well as reported data for ten additional Mayan languages from across the family, we offer a new approach to these effects. Our analysis builds theoretically on recent work tracing person/animacy restrictions to the nature of featural representations and the operation Agree, bringing this literature together with current understandings of Mayan syntax and the high-/low-absolutive parameter. We argue that the cross-Mayan data—relative hierarchy effects holding in the same way across both high-absolutive and low-absolutive languages—are best handled by, and bring new support for, an interaction/satisfaction approach to Agree and hierarchy effects (Deal 2024). Our analysis also casts new light on key topics in Mayan syntax, including the proper analysis of ergativity and the nature of obviation effects (Aissen 1997).
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".