Consistencia epistémica del síndrome de Dificultades del Aprendizaje: aportaciones de la magnetoencefalografía como técnica de neuroimagen funcional* Epistemics for Learning Disabilities: Contributions from Magnetoencephalography, a Functional Neuroimaging Tool
Bibliographic record
Abstract
The syndrome known as Learning Disabilities (LD) was described by S. Kirk in 1963. From that point on, institutions from the US, Canada and Spain have engaged in refining the concept and classification of LDs. The Com-plutense University in Spain, has proposed a descriptive and all-embracing definition, and has studied the different manifestations of LD, pursuing the description of biological markers and neurological features of LD’s main expressions: dyslexia, dyscalculia, dysorthographia, Attention Deficit and Hyperactivity Disorder –ADHD, and so forth. Findings in LD using functio-nal neuroimaging techniques, namely Magnetoencephalography (MEG), are described. MEG is a non-invasive technique, which records magnetic fields naturally generated by the brain and their spatial distribution. It allows simultaneous functional and structural information. MEG is therefore used in the study of primary and superior cognitive functions, in surveillance of patterns of normal cognitive function and those specific to the different LD clinical manifestations.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".