Las variedades del español en la enseñanza como lengua segunda/extranjera
Bibliographic record
Abstract
L’espagnol est une langue si etendue qu’elle comporte une pluralite de normes, toutes aussi reconnues les unes que les autres. Une des questions auxquelles doivent alors faire face les professeurs d’espagnol est quelle variete enseigner a leurs eleves, ou plutot, quelles varietes. L’objectif de la presentation est de guider les professeurs ou futurs professeurs d’espagnol langue seconde/etrangere (EL2/ELE) dans la creation d’un modele linguistique a utiliser en classe, en presentant d’abord quelques concepts linguistiques de base sur les varietes de langue. Nous aborderons alors la notion de variation et les differents types de varietes linguistiques ; et nous exposerons le modele linguistique d’enseignement d’Andion Herrero (2007), qui se base sur trois elements : l’espagnol standard, la variete preferentielle et les varietes peripheriques. Nous croyons en l’importance que les eleves developpent la capacite de comprendre leurs interlocuteurs peu importe leur variante et a se faire comprendre a leur tour. L’article fera donc un survol de la question des varietes dans les cours d’EL2/ELE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".