Creative ecologies and education futures
Bibliographic record
Abstract
The challenge to foster greater creativity in education systems represents a range of diverse and complex affordances and constraints. Creativity research in education spans policy, teaching, learning and assessment, as well as environments within and beyond the school that promote creative encounters. Worldwide, creativity, critical thinking, and problem-solving skills are marked as essential for effective learners and future employees. Creativity is closely linked with the development of flexible thinking and lateral problem-solving. Yet a shift is occurring from interest in creative individuals to creative ecologies in sociocultural formations of digitally networked cultures and collaborative methods of thinking. The value of attending to increasing creative sociality within and between diverse cultures and contexts is growing. Drawing on an international study of creativity in secondary schools across Australia, Canada, Singapore, and the United States, the authors argue that because creativity in education is central to lifelong learning and work satisfaction, schools must radically shift toward a more interdisciplinary whole-school creative ecology approach, and away from siloed disciplinary and individualist learning. The chapter draws on aspects of creative ecologies in education that combine science, technology, arts, culture, and industry, showing creativity as a fundamental aspect of education across all domains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".