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Record W2104910031 · doi:10.36510/learnland.v6i1.581

Enabling Creativity in Learning Environments: Lessons From the CREANOVA Project

2012· article· en· W2104910031 on OpenAlex
John M. Davis, Vinnarasan Aruldoss, Lynn McNair, Nikolaos Bizas

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueLEARNing Landscapes · 2012
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsnot available
Fundersnot available
KeywordsCreativityFlexibility (engineering)Knowledge managementCurriculumThe artsWork (physics)Creativity techniqueEuropean unionCollaborative learningEngineering ethicsEngineeringComputer sciencePedagogySociologyPsychologyBusinessPolitical scienceManagementSocial psychology

Abstract

fetched live from OpenAlex

The paper employs data from a European Union funded project to outline the different contexts and factors that enable creativity and innovation. It suggests that creativity and innovation are supported by flexible work settings, adaptable learning environments, collaborative design processes, determined effort, and liberating innovative relationships. It concludes that learning environments that seek to enable creativity and innovation should encourage collaborative working, offer flexibility for both learners and educators, enable learner-led innovative processes, and recognize that creativity occurs in curriculum areas beyond the creative arts.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.057
GPT teacher head0.363
Teacher spread0.307 · 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