Representative Bureaucracy in Action: Country Profiles from the Americas, Europe, Africa and Asia
Bibliographic record
Abstract
Contents: 1. Representative Bureaucracy: Concept, Driving Forces, Strategies B. Guy Peters, Eckhard Schroeter and Patrick von Maravic PART I: THE AMERICAS 2. Representative Bureaucracy in the United States B. Guy Peters 3. Representative Bureaucracy in Canada Luc Turgeon and Alain-G. Gagnon 4. Representative Bureaucracy in Mexico Maria del Carmen Pardo PART II: EUROPE 5. Representative Bureaucracy in Belgium: Power Sharing or Diversity? Steven van de Walle, Sandra Groeneveld and Lieselot Vandenbussche 6. Representative Bureaucracy in Transitional Bureaucracies: Bulgaria and Romania Katja Michalak 7. Representative Bureaucracy in Germany? From Passive to Active Intercultural Opening Patrick von Maravic and Sonja M. Dudek 8. Representative Bureaucracy in Italy Giliberto Capano and Nadia Carboni 9. Representative Bureaucracy in the Netherlands Frits M. van der Meer and Gerrit S.A. Dijkstra 10. Representative Bureaucracy in Switzerland Daniel Kubler 11. Representative Bureaucracy in the United Kingdom Rhys Andrews PART III: AFRICA, OCEANIA, AND ASIA 12. Representative Bureaucracy in South Africa Robert Cameron and Chantal Milne 13. Politics of Representative Bureaucracy in India Bas van Gool and Frank de Zwart 14. Bureaucratic Representation in Israel Moshe Maor 15. Representative Bureaucracy in Australia: A Post-Colonial, Multicultural Society Rodney Smith Bibliography 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".