La formación de profesores de español en Quebec: experiencias y balance
Bibliographic record
Abstract
En octubre del ano 2004, las personas que nos interesamos por la ensenanza del espanol en la provincia de Quebec tuvimos una oportunidad unica: encontrarnos todos para una jornada de intercambio y discusion, gracias a una iniciativa de la Universidad de Montreal. Habia alli mas de un centenar de personas, todas conectadas con el espanol: profesores de CEGEP, de distintas universidades, de escuelas secundarias, alumnos de todo tipo de programas, funcionarios del Ministerio de Educacion y hablantes nativos con ganas de integrarse a la gran empresa de la ensenanza y difusion del espanol. Esta reunion nos permitio conocernos, comparar situaciones y descubrir un sentimiento generalizado de descontento y frustracion que todos sospechabamos que existia, pero no con la unaniminidad y profundidad con las que se manifesto alli. Sobre este sentimiento quiero discutir en este breve e informal articulo, sobre su historia y sus causas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".