Currículos matemáticos en países francófonos: Desigualdades políticas e implicaciones para grado 1 y grado 2
Bibliographic record
Abstract
This paper presents a qualitative study of four Grade 1 and Grade 2 national mathematics curricula coming from francophone countries: Côte d’Ivoire, Djibouti, Canada (Québec), and France. A comparative analysis was performed to identify differences that potentially lead to inequities among the countries. We identified all the concepts present in the Grade 1 and Grade 2 mathematics curriculum using the mathematical literacy framework developed by the OECD through its PISA assessment: quantity, change and relationship, data and uncertainty, and space and shape. Then we looked across countries to find the major differences among them. The findings show three categories of possible inequities among the different curricula: some mathematical concepts are not presented in culturally relevant ways to students, some concepts present in the mathematics curricula are not mathematical concepts, and some important mathematical concepts are not part of the mathematics curricula. Our results show that these inequities appear in all four national curricula analysed here. Those findings highlight the need to question the political inequities among mathematics curricula around the world in order to give a chance for all students to learn strong and meaningful mathematics.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".