Preparing school leaders for the 21st century : an international comparison of development programs in 15 countries
Bibliographic record
Abstract
Abstract Preface Terms and Abbreviations Lists of Figures and Tables I. Context of Research Stephan Huber 1. School Leadership: Roles, Tasks, Competences, and Conceptions 2. Aim and Scope of this International Comparison 3. Methodology and Methods II. Comparison and Discussion of Findings Stephan Huber 1. Provider: Monopoly or Market, Centralised or Decentralised Planning and Implementation of Programs 2. Target Group, Timing, Nature of Participation, and Professional Validity: Multiple Approaches 3. Aims: Different Goals, Orientations, and Conceptions 4. Contents: What Is Important? 5. Methods: Learning in Lectures, Seminars, Workshops, from Colleagues, and in the Workplace 6. Pattern: One Size for All or Multi-Phase Designs and Modularisation III. Conclusions and Outlook Stephan Huber 1. Current Trends from a Global Perspective 2. Evaluation, Best Practice, and Multi-Stage Adjustment of Aims in School Leader Development 3. Recommendations for Designing and Conducting Training and Development Programs 4. What Has to be Done by Research? IV. Country Reports Europe Sweden: Split Responsibility between State and Municipalities Stephan Huber and Olof Johansson Denmark: No Need for Regulations and Standards? Stephan Huber and Lejf Moos England: Moving Quickly towards a Coherent Provision Stephan Huber and Mel West The Netherlands: Diversity and Choice Stephan Huber and Jeroen Imants France: Recruitment and Extensive Training in State Responsibility Stephan Huber and Denis Meuret Germany: Courses at the State-Run Teacher Training Institutes Stephan Huber and Heinz Rosenbusch Switzerland: Canton-Specific Qualification for Newly Established Principalships Stephan Huber and Anton Strittmatter Austria: Mandatory Training according to State Guidelines Stephan Huber and Michael Schratz South Tyrol, Italy: Qualifying for 'Dirigente' at a Government-Selected Private Provider Stephan Huber and Michael Schratz Asia Singapore: Full-Time Preparation for Challenging Times Stephan Huber and S. Gopinathan Hong Kong: A Task-Oriented Short Course Stephan Huber and Huen Yu Australia/New Zealand New South Wales, Australia: Development of and for a 'Learning Community' Stephan Huber and Peter Cuttance New Zealand: Variety and Competition Stephan Huber and Jan Robertson North America Ontario, Canada: Qualifying School Leaders according to Standards of the Profession Stephan Huber and Kenneth Leithwood Washington, New Jersey, California, USA: Extensive Qualification Programs and a Long History of School Leader Preparation Stephan Huber Washington: Working Together to Prepare Leaders Stephan Huber and Kathy Kimball New Jersey: A New Paradigm for Preparing School Leaders Stephan Huber and Michael Chirichello California: University- and State-Supported Professional Development Stephan Huber and Janet Chrispeels Short Summaries of Country Reports: A Juxtaposition Summary of the Book Information about Co-Authors Appendix: Methodology and Methods References Index
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".