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Record W2245241015 · doi:10.4324/9780203838945

Researching Creative Learning

2010· book· en· W2245241015 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsnot available
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

It is a common ambition in society and government to make young people more creative. These aspirations are motivated by two key concerns: to make experience at school more exciting, relevant, challenging and dynamic; and to ensure that young people are able and fit to leave education and contribute to the creative economy that will underpin growth in the twenty-first century. Transforming these common aspirations into informed practice is not easy. It can mean making many changes: turning classrooms into more exciting experiences; introducing more thoughtful challenges into the curriculum; making teachers into different kinds of instructors; finding more authentic assessment processes; putting young people’s voices at the heart of learning. There are programmes, projects and initiatives that have consistently attempted to offer such change and transformation. The UK programme Creative Partnerships is the largest of these, but there are significant initiatives in many other parts of the world today, including France, Norway, Canada and the United States. This book not only draws on this body of expertise but also consolidates it, making it the first methodological text exploring creativity. Creative teaching and learning is often used as a site for research and action research, and this volume is intended to act as a textbook for this range of courses and initiatives. The book will be a key text for research in creative teaching and learning and is specifically directed at ITE, CPD, Masters and doctoral students.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.247
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.002
Insufficient payload (model declined to judge)0.0420.004

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 head0.063
GPT teacher head0.431
Teacher spread0.368 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations27
Published2010
Admission routes1
Has abstractyes

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