INTRODUCCIÓN DEL CONCEPTO DE COMPETENCIA EN EL ANÁLISIS DIDÁCTICO DE LAS PRÁCTICAS DE ENSEÑANZA: EL CASO DE LA ENSEÑANZA DE CIENCIAS HUMANAS EN LA EDUCACIÓN BÁSICA DE QUEBEC
Bibliographic record
Abstract
Usando como ejemplo la enseñanza de las Ciencias Humanas en la educación básica en Quebec, los autores ofrecen una mirada crítica en relación a la relevancia y al rol adquiridos por los saberes escolares disciplinarios en el desarrollo de competencias en los alumnos. Lo anterior enmarcado en alguno de los tres modelos de competencia descritos en la primera sección del presente artículo. En la segunda parte, se definen las dimensiones utilizadas en el estudio y a continuación se describe la conformación de las disciplinas que resultan de cada uno de los modelos de competencia de acuerdo a las dimensiones seleccionadas. Se concluye con algunas claves con las que sería posible extender así como adaptar el marco de análisis didáctico en la enseñanza basada en competencias.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".