The New Meaning of Educational Change
Bibliographic record
Abstract
This new edition is unlike any of its predecessors. Michael Fullan first provides a critical account of the last 60 years of educational change across the world with a focus on the United States. He then presents a radically different future, including a complete model for transforming our badly outdated current education system. “A searing synthesis of what we now know about system change.” —Anthony Mackay, Centre for Strategic Education, Melbourne “This definitive book articulates in plain language where we need to go and how to get there.” —Michael Matsuda, superintendent, Anaheim Union High School District, CA “Fullan captures the complexity of leadership in a time of technological advancements and complex societal challenges.” —Thomas D’Amico, director of education, Ottawa Catholic School Board “Run, don’t walk, to pick up and read the sixth edition of The New Meaning of Educational Change!” —Barnett Berry, Learning Policy Institute “Michael Fullan brings the reader on a compelling 60-year journey of educational change.” —Tiffany Bastin, assistant deputy minister, New Brunswick Department of Education, NJ “I have no doubt that this sixth edition will continue Fullan’s enormous success in influencing policy and practice globally.” —Mel Ainscow, emeritus professor, University of Manchester, UK
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 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".