Analysing French Teacher Communities Producing Online Resources: New perspectives on teacher agency
Bibliographic record
Abstract
Online resources have become an important reality in education, accompanied by the phenomenon of the production, modification, and wide dissemination of educational resources by communities of teachers. This article explores the situation in France. First, we recall previous research on how teachers cooperate in order to create online resources. We distinguish between several types of collectives (captive communities, activist communities, proto-communities) and focus on their dynamics. Doing so implies considering the subjects as well as the instruments used and the social systems within which they evolve. Second, the issue of teacher agency relative to educational resources is analysed. In France, teachers are granted great freedom of pedagogical methods, a freedom not seen in all countries. Therefore, we analyse the activities of these collectives to understand how they view their activity and their role, specifically when using learning materials in class. An important related issue is the eagerness of administration to rely on evidence-based practice for orienting teacher action. We suggest that participatory research is a good investment for raising meaningful issues and proposing possible, short-term solutions.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| 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".