Bibliographic record
Abstract
Change in education is too often a process which enthusiasts, ranging from top policy makers to groups of teachers, plan and drive forward, but in which they all find unexpected pitfalls. Every innovation depends on the commitment of schools and teachers to make it work. But often that commitment is lacking, or is less than total, or it turns to fustration as events develop. This book is based on a set of stories from teachers and education professionals in thirteen OECD countries. Twenty-three case studies of educational innovation in science, mathematics and technology have involved school teachers, inspectors, academics (both subject specialists and educational researchers), policy makers and advisors. The case studies come from Australia, Canada, France, Germany, Ireland, Japan, the Netherlands, Norway, Scotland, Spain, Switzerland and the USA. Drawing on this rich variety of material the authors concentrate on the origins and purposes of innovation within and across the science, mathematics and technology curricula. They consider the conceptions of the three subjects, along with issues of teaching, learning and assessment, and explore the involvement of both teachers and students. They reflect on the various strategies adopted to cope with or bring about change, and offer valuable insights to advisors, developers, policy makers and practitioners, both in schools and outside. The writing team includes Paul Black, King's College London; Mike Atkin, Stanford University; Raymond Duval, University of Lille; Edwyn James, Consultant, OECD; John Olson, Queen's University of Kingston, Ontario; Dieter Pevsner, Consultant, London; Senta Raizen, National Centre for Improving Science Education, Washington; Maria Saez, University of Valladolid, Spain; and Helen Simons, Southampton University. Published in association with the OECD
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 0.011 |
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".