A Global Dialogue on Federalism Booklet Series <i>Volume II</i>
Bibliographic record
Abstract
These lively, timely, and accessible dialogues on federal systems provide a comparative snapshot of each topic and include comparative analyses, glossaries of country-specific terminology, and a timeline of major constitutional events. Countries considered include Argentina, Australia, Austria, Belgium, Brazil, Canada, Germany, India, Mexico, Nigeria, Russia, South Africa, Spain, Switzerland, and the United States. Whether you are a student or teacher of federalism, working in the field of federalism, or simply interested in the topic, these booklets will prove to be an insightful, brief exploration of the topic at hand in each of the featured countries. Contributors include Sarah Byrne (Université de Fribourg), Marcelo Piancastelli de Siqueira (Institute for Applied Economic Research, Brasillia), Hugues Dumont (Facultés Universitaires Saint-Louis, Brussels), J.Isawa Elaigwu (Institute of Governance and Social Research, Jos), Thomas Fleiner (Université de Fribourg), Xavier Bernadi Gil (Universitat Pompeu Fabra, Barcelona), Ellis Katz (International Association of Centers for Federal Studies, PA), Nicolas Lagasse (Facultés Universitaires Saint-Louis, Brussels), Clement Macintyre (University of Adelaide), George Mathew (Institute of Social Sciences, New Delhi), Manuel González Oropeza (Universidad Nacional Autónoma de México), Hans-Peter Schneider (Universität Hannover), Richard Simeon (University of Toronto), Clara Velasco (Universitat Pompeu Fabra, Barcelona), Ronald L. Watts (Queen's University), and John Williams (Australian National University, Canberra).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.082 | 0.023 |
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".