Numerical skills and dyscalculia. From basic research to practice in Cuba ( <i>Habilidades numéricas y discalculia. De la investigación básica a la práctica en Cuba</i> )
Bibliographic record
Abstract
Establishing bridges between the findings from cognitive neurosciences and teaching practice has not been systematically achieved. However, many researchers interested in this area agree on the positive impact that knowledge on how the brain learns has on teaching practices and educational policies. For more than 15 years, the Laboratory for Educational Neurosciences from the Cuban Centre for Neurosciences has collected evidence on basic numerical capacities and their association with learning mathematics, taking into account different levels of analysis that consider biology, cognition and education. Researchers in this laboratory have developed a conceptual, methodological and instrumental platform based on the experimental evidence they have systematically obtained. This platform has resulted in the design and validation of tools and resources for learning mathematics in the classroom with the intervention of the teachers.
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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 | 0.006 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".