Bibliographic record
Abstract
This paper offers an in-depth look at roots and verb stem morphology in Chuj (Mayan) in order to address a larger question: when it comes to the formation of verb stems, what information is contributed by the root, and what is contributed by the functional heads? I show first that roots in Chuj are not acategorical in the strict sense (cf. Borer 2005), but must be grouped into classes based on their stem-forming possibilities. Root class does not map directly to surface lexical category, but does determine which functional heads (i.e. valence morphology) may merge with the root. Second, I show that while the introduction of the external argument, along with clausal licensing and agreement generally, are all governed by higher functional heads, the presence or absence of aninternalargument is dictated by the root. Specifically, I show that transitive roots in Chuj always combine with an internal argument, whether it be (i) a full DP, (ii) a bare pseudo-incorporated NP, or (iii) an implicit object in an antipassive. In the spirit of work such as Levinson (2007, 2014), I connect this to the semantic type of the root; root class reflects semantic type, and semantic type affects the root’s combinatorial properties. This work also contributes to the discussion of how valence morphology operates. In line with works such as Alexiadou, Anagnostopoulou & Schäfer (2006), I argue that valence morphology applies directly to roots, rather than to some ‘inherent valence’ of a verb.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| 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".