Les interactions entre la pédagogie critique et la théorie socioculturelle de l'activité comme cadre d'analyse de l'innovation des pratiques enseignantes
Bibliographic record
Abstract
Several challenges face teachers who wish to change their practices to move away from the top-down approach. These challenges are more associated with cultural factors than individual factors, including the school environment and the educational systems in place. To understand how these cultural elements influence the choice of teaching practices and the willingness of teachers to do things differently, the framework of cultural-historical activity theory is interesting. However, it is possible to improve it with regard to the political aspects of education using certain concepts of the critical pedagogy philosophy. This article discusses possible contributions of critical pedagogy to the framework of cultural-historical activity theory in addressing the challenges associated with teaching innovation. While many elements of teaching practice can be studied from the perspective of these two streams, a literature rewiew on their complementarities and similarities has identified two interrelated themes: the effect of political forces on the choice of teaching approaches and the importance of questioning in changing mindsets. At the end of the analysis, we were able to map out the stages of individual consciousness-raising using important concepts from the third generation of cultural-historical activity theory.
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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 | 0.032 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.078 |
| Scholarly communication | 0.026 | 0.020 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".