MétaCan
Menu
Back to cohort
Record W2187723838 · doi:10.36510/learnland.v5i1.542

The Impact of Emotions on Divergent Thinking Processes: <br>A Consideration for Inquiry-Oriented Teachers

2011· article· en· W2187723838 on OpenAlexaffvenue
Krista Ritchie, Bruce M. Shore, Frank LaBanca, Aaron J. Newman

Bibliographic record

VenueLEARNing Landscapes · 2011
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsMcGill UniversityDalhousie UniversityIzaak Walton Killam Health Centre
Fundersnot available
KeywordsCreativityCornerstoneNeurocognitiveDivergent thinkingPlan (archaeology)Convergent thinkingProcess (computing)PsychologyCreative thinkingMathematics educationPedagogyEngineering ethicsCognitionComputer scienceSocial psychologyEngineering

Abstract

fetched live from OpenAlex

Innovation is a cornerstone of the success of our global society and it is required to generate solutions to today’s challenges. Students will benefit from classrooms that encourage creative thought and innovative self-directed projects. Inquiry is an instructional approach that fosters creativity and divergent thinking. This paper elaborates on one aspect of the creative process—the impact of emotions on divergent thinking. Theory and some existing research are reviewed and a plan for a neurocognitive study using electroencephalography is delineated. Current and previous research is taken into account when reflecting on suggestions for fostering learning environments conducive to creativity and building interdisciplinary collaboration.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.006
Scholarly communication0.0070.004
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.061
GPT teacher head0.312
Teacher spread0.251 · 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 designObservational
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

Citations2
Published2011
Admission routes2
Has abstractyes

Explore more

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