Administering the summit : administration of the core executive in developed countries
Bibliographic record
Abstract
Notes on the Contributors List of Figures List of Tables INTRODUCTION Staffing the Summit - the Administration of the Core Executive: Convergent Trends and National Specificities B.G.Peters, R.A.W.Rhodes & V.Wright WESTMINSTER SYSTEMS Administering the Summit: The British Prime Minister's Office C.Clifford Administering the Summit from a Canadian Perspective D.Savoie & B.G.Peters Administering the Summit: Australia P.Weller OTHER PARLIAMENTARY SYSTEMS Management of Politics in the German Chancellor's Office F.Muller-Rommel The Prime Minister's 'Staff': The Case of Italy S.Cassese A Quasi-Presidential Premiership: Administering the Executive Summit in Spain P.Heywood & I.M.Alvarez Sweden: The Quest for Co-ordination E.Page & N.Elder How Informal Can You Be?: The Case of Denmark T.Knudsen Administering the Summit: The Greek Case D.Sotiropolous Serving the Japanese Prime Minister I.Neary PRESIDENTIAL AND SEMI-PRESIDENTIAL SYSTEMS Staffing the Summit: France R.Elgie Administering the Summit in the United States B.Rockman CONCLUSION The Struggle for Control 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.137 | 0.051 |
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".