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Record W4319161790 · doi:10.35542/osf.io/g8ek7

Reflections on the past two decades of Mind, Brain, and Education

2023· preprint· en· W4319161790 on OpenAlexaboutno aff
Ola Ozernov‐Palchik, Courtney Pollack, Elizabeth Bonawitz, Joanna A. Christodoulou, Nadine Gaab, John D. E. Gabrieli, Patricia Monticello Kievlan, C.A. Kirby, Grace C. Lin, Gigi Luk, Charles A. Nelson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsnot available
Fundersnot available
KeywordsField (mathematics)Graduate educationGraduate studentsPsychologyEngineering ethicsSociologyPolitical sciencePedagogyEngineering

Abstract

fetched live from OpenAlex

In the early 2000s, Kurt Fischer and colleagues founded the Mind, Brain, and Education (MBE) field (Blake & Gardner, 2007; Fischer et al., 2007), including a flagship journal, society (International Mind, Brain, Education Society [IMBES]), and a master's degree program at the Harvard Graduate School of Education (Harvard). The MBE program was first-of-its-kind, focused on the intersection of neurobiology, psychology, and educational research and practice (Blake & Gardner, 2007; Fischer, 2009). Between its first cohort in 2004 and its final cohort in 2022, the program graduated 668 students from around the world (see Figure 1). Contemporaneously, scholars developed MBE or related Educational Neuroscience initiatives in several U.S. states, Canada, the UK, Austria, The Netherlands, China, Israel, across Latin America, and other locations around the world.In 2022, the MBE masters program was integrated into a broader human development and education offering at Harvard. Program faculty and alumni gathered in a virtual event to mark the sunsetting of the program and to reflect on advancements in the MBE field. This commentary grew out of that meeting and reflects the perspectives of individuals affiliated with MBE on the evolution and impact of the field, with an eye toward future directions. Specifically, to guide their reflections, all authors addressed the following questions: 1.How has the field of MBE changed, and what developments particularly excite you in your area of the field? 2.What do you regard as the most significant impact of MBE on educational practice?3.As you look ahead, what potential advancements or emerging trends do you envision for MBE research and its practical applications?

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.148
GPT teacher head0.411
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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Same topicNeuroscience, Education and Cognitive FunctionFrench-language works237,207