Bibliographic record
Abstract
En el presente estudio se discute la adquisición de los clasificadores numerales -eb’, -k’on y -wan en q’anjob’al evaluando datos de tres niños de las siguientes edades: Xhuw (1;9-3;0), Xhim (2;3-4;0) y Tum (2;7- 3;6). Los resultados muestran que en este rango de edades los niños comienzan a usar de forma gradual y esporádica estos clasificadores, aunque los errores de uso muestran que utilizan el sistema de clasificadores numerales de modo diferente a los adultos, dado que tienen problemas al asignar su valor semántico en la clasificación de seres humanos, animales y cosas. La poca frecuencia de -k’on (clasificador para animales) y -wan (para seres humanos), y el uso frecuente de -eb’ (para cosas) en el habla parental podría ser el motivo del uso de -eb’ como una forma dominante en los datos de los niños. También cometen errores en el uso de heb’ (propio de seres humanos) para pluralizar seres humanos y animales, lo cual no se esperaría entre adultos q’anjob’ales.
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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 | 0.017 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".