Managing change in the public services
Bibliographic record
Abstract
This book explores the management of change to improve public service effectiveness. It breaks new ground in addressing why public service change is becoming increasingly complex to manage, how people cope with this new complexity, what implications arise for improving policy and practice, and which avenues for further research and theory-building look particularly promising. The contributors are all leading researchers from the USA, Canada and the UK. Together they provide a synthesis of state-of-the-art thinking on the complex change process in Anglo-American contexts, policy-making for public service reform that generates managerial complexity, and practice in service organizations to improve provision. Special reference is made to education and health: the largest and most complex of the public services. The analysis has wider relevance for other public services and national contexts. Managing Change in the Public Services is essential reading for all concerned with public service improvement - leaders and managers in service organizations, administrators, trainers, advisers and consultants who support the management of change, policy-makers and public servants, and advanced course students and academics. The book also offers general insights for the theory and practice of managing organizational and systemic change.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".