La interacción fonética en idioma italiano y español : aspectos pragmáticos dentro de la clase
Bibliographic record
Abstract
El aprender italiano por estudiantes de habla hispana tiene varios matices y contrastes, porque son idiomas que se parecen y confunden por sus analogias y similitudes y expone al estudiante a cometer errores en la comprension y pronunciacion de textos orales.Como lo dice Calabro: en cuanto a la experiencia de los sonidos “in quanto hanno fatto esperienza dei suoni della L2 in modo fisico, tattile, visivo, uditivo” (Calabro L. , 2016). La interaccion fonetica dentro del aula permite relacionar los diferentes sonidos que son representados por una facil simbologia que el estudiante pueda reconocer, analizar y pronunciar como parte del mejoramiento de la pronunciacion eficiente de las letras individuales y las uniones de las letras formando palabras. El objetivo es detectar la correcta o erronea pronunciacion de sonidos en idioma italiano, valorarlos y mejorarlos a traves de la interaccion oral y anotarlos en matrices de valoracion que cada estudiante maneja personalmente dentro de las aulas de clases. En la metodologia, estudiantes en un numero de 30 de nivel B1 de italiano, usan las matrices de tres columnas, donde analizan, critican, evaluan y usando el feedback reestructuran los errores como aseguran los autores Alberta Novello y PaoloBalboni “le competenze mentali si traducono in azione comunicativa, nel saper fare lingua quando esse vengono utilizzate per comprendere, produrre, manipolare testi” (Balboni, 2015). “La natura del feedback e, difatti, il primo elemento da prendere in considerazione nel momento in cui si stabilisce la necessita di una valutazione”. (Novello, 2014)
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".