Bibliographic record
Abstract
While many books address the 'what' of change in education, this addresses the 'how'. The pressure for continuous adaptation and innovation in education is relentless, yet there is more failure in implementation of change than success. These failures are damaging to staff and students, as well as costly. Change Matters offers a practical guide to change management for teachers and administrators across all education sectors and for training managers in workplace settings.Change Matters assists educators to develop their abilities to manage their own change projects, and also to help their organisations to manage their overall improvement and innovation activities. Geoff Scott draws on successful experience to create a framework for the educational change process. He shows how to initiate, develop, implement and evaluate a new learning program, and how to manage continuous quality improvement and innovation at the organisational level. The need for leadership is assessed, and the particular circumstances of workplace trainers are discussed. The book is illustrated with case studies and reflective exercises which can be used individually or with other educators.'An eminently readable and practical guide for those who want to make sure that the educational changes they attempt really do make a difference for their students. Highly recommended.' - Professor Michael Fullan, Dean, Ontario Institute for Studies in Education, University of Toronto, and author of The New Meaning of Educational Change and of the What's Worth Fighting For trilogy with Andy Hargreaves.
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.189 | 0.125 |
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".