Can the Differences Between Education and Neuroscience be Overcome by Mind, Brain, and Education?
Bibliographic record
Abstract
ABSTRACT—The new field of Mind, Brain, and Education (MBE)—sometimes called educational neuroscience—is posited as a mediator between neuroscience and education. Several foundational concerns, however, can be raised about this emerging field. The differences between neuroscience and education are many, including differences in their histories, philosophies, and epistemologies. Historically, science and education have demonstrated separate, but interwoven, influences on society; philosophically, the values by which they operate are often in opposition; and epistemologically, the fields have relied on different conceptualizations of knowledge. Discussion about these differences has been largely absent in attempts to promote MBE. Two steps are proposed to respond to this omission. First, encouraging discussion about disciplinary differences and assumptions may enable better understanding between disciplines and facilitate the establishment of a more collaborative research community. Second, atransdisciplinaryframework that focuses on salient issues of interest across disciplines should be considered. Transdisciplinarity aims for the creation of an inclusive research environment that transcends traditional disciplinary approaches to complex problems. This article initiates an exploration of disciplinary differences and proposes commitment to transdisciplinarity as a guiding principle that may increase the viability of MBE as a mediating field between neuroscience and education.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".