La modelación e interpretación del enfriamiento en libros de texto de matemática para secundaria y universidad: unas consideraciones críticas
Bibliographic record
Abstract
espanolEn este trabajo se analiza el uso de los cambios de temperatura (enfriamiento) en problemas matematicos de libros de texto del nivel secundario (Mexico) y universitario (Estados Unidos de America y Canada). Se presenta una evidencia experimental que pone en entredicho las supuestas situaciones reales que se exponen en aquellos documentos, evidenciando que el uso inapropiado del fenomeno de enfriamiento no es un sindrome mexicano sino que podria ser una “cultura global” que requiere mas atencion de la comunidad internacional dedicada a la educacion matematica. Se incluye una discusion de las implicaciones negativas que pueden llegar a tener el uso inapropiado de este contexto en la ensenanza y el aprendizaje de la matematica EnglishThis paper analyzes the use of temperature changes (cooling) in mathematical problems of textbooks of the secondary level (Mexico) and university level (United States of America and Canada). An experimental evidence is presented that calls into question the supposed real situations that are exposed in those documents, showing that the inappropriate use of the cooling phenomenon is not a Mexican syndrome but it could be a “global culture” that requires more attention from the international community dedicated to education mathematics. A discussion of the negative implications that may have the inappropriate use of this context in the teaching and learning of mathematics is included
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".