Making clear teachers' experience-led wisdom in the context of teaching practicum: a conceptual and methodological framework
Bibliographic record
Abstract
Teachers lack the appropriate tools to acurately express their practice in words. It is important to illustrate and communicate the knowledge used in the practice of teaching to give access to the experience. This article presents the conceptual and methodological framework of a study inspired by the practical argumentation approach, and the analysis of the teaching practice of the participants by means of shared thoughts as well as the analysis of video recordings. Receved: 08 –06-04/ Accepted: 25/08/04 How to reference this article: Gervais, C. & Correa Molina, E. (2004). Explicitación del saber de experiencia de los profesores en el contexto de las prácticas docentes: un marco conceptual y metodológico. Íkala. 9(1), pp. 141 – 167
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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 | 0.048 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.010 | 0.079 |
| Scholarly communication | 0.027 | 0.028 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".