Bibliographic record
Abstract
Teachers, governments and employers around the world all aspire 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 ensuring that young people are able and fit to leave education and contribute to the creative economy which will underpin growth in the twenty-first century. \n \nTransforming these common aspirations into informed practice is not easy. It can mean making many changes: \n \n \nturning classrooms into more exciting experiences \n \nintroducing more thoughtful challenges into curriculum \n \nmaking teachers into different kinds of instructors \n \nfinding more authentic assessment processes \n \nputting young people's voice at the heart of learning. \nThere are programmes, projects and initiatives which have consistently attempted to offer such change and transformation. The English programme, Creative Partnerships, is the largest of these, but there are significant initiatives in many other parts of the world including France, Norway, Canada and the USA. This book not only draws on this body of expertise but also consolidates it, making it the first methodological text exploring creativity. \n \nCreative teaching and learning is often used as a site for research and action research, and this volume is intended to act as a text book 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.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".