The Didactics of Mathematics: Approaches and Issues
Bibliographic record
Abstract
This book, the outcome of a conference organised in 2012 in Paris as a homage to Michèle Artigue, is based on the main components of this event. However, it offers more than a mere reflection of the conference in itself, as various well-known researchers from the field have been invited to summarize the main topics where the importance of Artigueâs contribution is unquestionable. Her multiple interest areas, as a researcher involved in a wider community, give to this volume its unique flavour of diversity. Michèle Artigue (ICMI 2013 Felix Klein Award, CIAEM 2015 Luis Santaló Award) is without doubt one of the most influential researchers nowadays in the field of didactics of mathematics. This influence rests both on the quality of her research and on her constant contribution, since the early 1970s, to the development of the teaching and learning of mathematics. Observing her exemplary professional history, one can witness the emergence, the development, and the main issues of didactics of mathematics as a specific research field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".