Constructivist developmental theory is needed in developmental neuroscience
Bibliographic record
Abstract
Neuroscience techniques provide an open window previously unavailable to the origin of thoughts and actions in children. Developmental cognitive neuroscience is booming, and knowledge from human brain mapping is finding its way into education and pediatric practice. Promises of application in developmental cognitive neuroscience rests however on better theory-guided data interpretation. Massive amounts of neuroimaging data from children are being processed, yet published studies often do not frame their work within developmental models-in detriment, we believe, to progress in this field. Here we describe some core challenges in interpreting the data from developmental cognitive neuroscience, and advocate the use of constructivist developmental theories of human cognition with a neuroscience interpretation.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.031 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".