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Building Mind, Brain, and Education Connections: The View From the Upper Valley

2009· article· en· W2111708914 on OpenAlexaff
Donna Coch, Stephen A. Michlovitz, Daniel Ansari, Abigail A. Baird

Bibliographic record

VenueMind Brain and Education · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsWestern University
Fundersnot available
KeywordsOutreachContext (archaeology)PedagogyVocabularyMathematics educationSociologyPsychologyPolitical scienceLinguisticsGeography

Abstract

fetched live from OpenAlex

ABSTRACT— This article describes the efforts of a small group of educators and researchers to build a model for making connections across mind, brain, and education. With a common goal of sharing, strengthening, and building useable knowledge about child and adolescent learning and development, we focused on questions of mutual interest to educators and researchers. We describe our efforts to develop a common vocabulary and language and to create opportunities for dialogue and discussion, including classes and talks for in‐service and preservice teachers, research laboratories open to in‐service and preservice teachers, local conferences that provided a context for educator and researcher interactions, and researcher outreach in the local education community at the administrative, classroom, and student levels. These activities represent concrete mechanisms by which links might be forged between educators and researchers within the context of Mind, Brain, and Education.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0130.032
Scholarly communication0.0190.010
Open science0.0020.013
Research integrity0.0060.012
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.029
GPT teacher head0.303
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations34
Published2009
Admission routes1
Has abstractyes

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