Errores y causas: usos de ser y estar en estudiantes francófonos de ELE
Bibliographic record
Abstract
Este trabajo se centra en el uso de los verbos ser y estar por parte de los estudiantes francófonos de español como lengua extranjera (ele) en Quebec. Utilizar correctamente estos verbos constituye una cuestión difícil de sortear para los estudiantes que poseen un sistema verbal atributivo diferente al del español, debido a la incidencia de factores gramaticales, léxico-semánticos y pragmáticos, así como al carácter cambiante de la lengua. Este artículo presenta un diagnóstico del proceso de aprendizaje de este rasgo gramatical específico y prueba la utilidad del análisis de errores como línea de investigación. Demuestra que la observación de la interlengua de los aprendices y el análisis de sus producciones, permiten establecer un panorama de las dificultades, encontrar sus causas y comprender el fenómeno de la interlengua. Con todo, nuestros resultados indican que la gran mayoría de los errores son de tipo intralingüístico: se encuentran en el sistema del español.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".