Making Schools Different: Alternative Approaches to Educating Young People
Bibliographic record
Abstract
What can we do with students who don't succeed in the typical classroom, and what are the alternatives to full-time schooling? With contributions from leading academics from Canada, America, the UK, The Netherlands and Australia, this internationally-minded book helps the reader to reflect on the ways young people are taught, and presents possible alternative approaches. Global social and economic changes and technological developments are driving the need for change within education, so that we can better cater for a diversity of young people. This book offers a forward-looking overview of where we are now, and where we might want to go in the future. It includes chapters on: - educational innovations; - learning identities; - learning spaces; - e-learning and remote students; - alternatives in education. This book will open your mind to the changing experience of schooling, and highlights new and different ways to help those whose needs simply don't fit into the usual mould. Suitable for all those on all undergraduate and postgraduate Education courses, and for those on Education Studies and Childhood and Youth courses, this book is an engaging, thought-provoking read.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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.
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".