Demostrativos nominales en Chuj de San Mateo Ixtatán
Bibliographic record
Abstract
En Chuj hay dos partículas deícticas, tik (proximal) y chi’ (distal) que se pueden añadir a una base nominal para formar dos tipos diferentes de demostrativos. Se añaden a una frase nominal definida para formar demostrativos de base definida (DBD) y a una frase nominal indefinida para formar demostrativos de base indefinida (DBI). Con esto sugerimos que los demostrativos en Chuj no forman una categoría básica, sino que se derivan de manera composicional (Hanink 2018 y Ahn 2019). Tradicionalmente, los demostrativos se han analizado como un subtipo de determinante definido (Roberts 2002, Wolter 2006, Elbourne 2008, 2013, Ahn 2019). La existencia de dos tipos de demostrativos en Chuj pone en duda una semántica unificada de los demostrativos basada en la definitud. Además, la literatura tipológica sobre los demostrativos distingue entre dos usos prototípicos: exofóricos y endofóricos (Kaplan 1989, Diessel 1999, Wolter 2009, Doran y Ward 2019, Ahn 2019). En este trabajo mostraremos que los DBI se usan sobre todo en contextos exofóricos, mientras que los DBD se prefieren en contextos anafóricos. Para llegar a estos resultados usamos una metodología basada en elicitaciones que involucran traducciones y solicitudes de juicios semánticos ante contextos específicos (Matthewson y Tonhauser 2015). También empleamos el cuestionario de Wilkins (1999, 2018) con preguntas dirigidas a la investigación de los demostrativos.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".