Le rôle de la philosophie de l'éducation dans la formation des maîtres
Bibliographic record
Abstract
We cannot talk about the role of philosophy of education in teachers' training programs without examining the relationship between philosophy and educational theories. Even when theoreticians like Kieran Egan, for example, claim that education is a normative discourse that cannot be inferred from "local" empirical psychological research, the role of philosophy seems of utmost importance because philosophy has essentially to do with norms and values. Another way of looking at the "sciences" of education that renews our comprehension of their relationship is by analysing a new cognitive psychological approach named "metacognition" which rests on one's ability to understand one's own cognitive strategies and mechanisms of regulation. The well-known psychologist Adrien Pinard in Quebec has been working on those concepts lately, inspired by Flavell's model of cognitive regulation. This approach is remarkably similar both to Wilson's definition of procedural criteria which he claims educational theory is all about and to the very specific method of philosophy, that of analytical and reflective thought. Philosophy or reflective thought cannot be separated from practice in teacher training since education is not only a matter of pedagogical means but also of attitudes to and relationship with another person. Thus, besides theory and practice, a third parameter should be taken into account in the elaboration of a teacher training program: the meta-practice and theory parameter, or the reflection on one's own practice, which is essentially philosophical.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.047 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".