Expanding the Typology of Absolutive Syntax in Mayan: Evidence From Northern Mam
Bibliographic record
Abstract
ABSTRACT Past work on Mayan languages has divided the family into two groups based on syntactic ergativity: ‘high‐absolutive’ languages in which objects raise to a position above the ergative subject and enter into Agree with a high probe and ‘low‐absolutive’ languages in which objects remain low and enter into Agree with a low probe. This object raising approach has been proposed to correlate with a constellation of syntactic properties, related to Ā‐extraction constraints, morpheme order, nonfinite embedding, and binding effects. This paper adds a third option to the typology based on data from a Northern Mam variety in which objects systematically fail to agree. Though it appears to fall outside of the established paradigm, I argue that this ‘no‐absolutive’ syntax is also directly predicted by an object raising analysis which locates variation in the presence or absence of [EPP] and ‐probe features on . To support this, I show how no‐absolutive Northern Mam patterns as we would expect across all previously proposed correlates of object raising. This paper functions as Part II to Royer and Coon 2025, also in this volume.
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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 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".