El Análisis Contrastivo como herramienta para facilitar el plurilingüismo: el caso del artículo en árabe y en español
Bibliographic record
Abstract
Este artículo tiene como objetivo presentar algunos de los beneficios que puede reportar el análisis contrastivo para la docencia de los profesores de español lengua extranjera a araboparlantes en sus países de origen. Siguiendo como eje el concepto de plurilingüismo establecido por el MCER (Marco común europeo de referencia para las lenguas: aprendizaje, enseñanza, evaluación) y el de conciencia lingüística, se presenta un ejemplo de cómo el conocimiento de los profesores de las similitudes y diferencias entre los usos de las formas lingüísticas puede contribuir al desarrollo de metodologías en las que se potencia el desarrollo de la competencia plurilingüe de los alumnos. Una competencia en la que los contenidos lingüísticos ayudan a lograr un mayor éxito comunicativo. En este caso, se estudiará la aportación del análisis contrastivo de los artículos definidos en ambas lenguas y su explotación desde el punto de vista estratégico. The aim of this article is to present the benefits of contrastive analysis studies in teaching Spanish as a foreign language to Arab-speaking students in their home countries. Considering the concept of multilingualism set by the CEFR (Common European Framework of Reference for Languages: Learning, Teaching, Assessment) and the notion of language awareness, this research is an example of how teachers’ knowledge of the similarities and differences between the uses of linguistic forms may contribute to the development of multilingual awareness of students. At the same time, linguistic contents will help to achieve a greater communicative success. In this case, we will consider the contributions of contrastive analysis of definite articles in both languages and its operation from the strategic standpoint.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| 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".