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Record W4409158053 · doi:10.1007/s11049-024-09648-y

Mayan animacy hierarchy effects and the dynamics of Agree

2025· article· en· W4409158053 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNatural Language & Linguistic Theory · 2025
Typearticle
Languageen
FieldPsychology
TopicCategorization, perception, and language
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsAnimacyHierarchySyntaxLinguisticsComputer sciencePerspective (graphical)PsychologyCognitive psychologyArtificial intelligencePolitical science

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.002
GPT teacher head0.280
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it