Evaluación docente, responsabilidad y aprendizaje profesional: la perspectiva canadiense
Bibliographic record
Abstract
Las concepciones tradicionales de la evaluación docente son problemáticas porque en gran medida desconocen la complejidad de la enseñanza y el aprendizaje. En Canadá, las concepciones en desarrollo de la evaluación docente incorporan un enfoque mucho más de equipo –entre colegas– y colaborativo para abordarla. Si bien estos modelos de evaluación docente reconocen la necesidad de asegurar la calidad, su principal propósito es motivar el aprendizaje y desarrollo profesional dentro de un marco evaluativo que guíe dicho aprendizaje. Ejemplos de programas de preparación docente así como las diversas provincias, destacan el potencial subyacente de los modelos de evaluación docente para enfocarse primordialmente en el crecimiento profesional dentro de un clima de responsabilidad.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.026 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".