Articulations between Tangible Space, Graphical Space and Geometrical Space
Bibliographic record
Abstract
This book aims to present some of the latest research in the didactics of space and geometry, deepen some theoretical questions and open up new reflections for discourse. Its focus is as much on the approach of geometry itself and its link with the structuring of space as it is on the practices within the classroom, the dissemination of resources, the use of different artefacts and the training of teachers in this field. We study how spatial knowledge, graphical knowledge and geometric knowledge are taken into account and articulated in the teaching of space and geometry in compulsory schools, teaching resources (programs and textbooks) and current teacher training. We question how the semiotic dimension (language, gestures and signs) of geometric activity can be taken into account, and we identify the role of artefacts (digital or tangible) in the teaching and learning of geometry. This book brings together some fifteen contributions from Frenchspeaking researchers from different countries (France, Switzerland and Canada).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".