Dialogues on local government and metropolitan regions in federal countries
Bibliographic record
Abstract
Contributors include Raoul Blindenbacher (Forum of Federations), Martin Burgi (Ruhr-University Bochum, Germany), Luis Cesar (Federal University of Rio de Janeiro, Brazil) Jaap de Visser (University of Western Cape, South Africa), Habu Galadima (University of Jos, Nigeria), Sol Garson (Federal University of Rio de Janeiro, Brazil), Boris Graizbord (National College of Mexico, Mexico), Rakesh Hooja (HCM Rajasthan State Institute of Public Administration and Principal Secretary to Government, India), Andreas Kiefer (European Affairs Office of the Land Salzburg, Austria), Andreas Ladner (Swiss Graduate School of Public Administration, Switzerland), George Mathew (Institute of Social Sciences, India), Thomas Minger (Conference of Cantonal Governments, Switzerland), Mike Pagano (University of Illinois at Chicago, United States), Chandra Pasma (Forum of Federations), Graham Sansom (University of Technology Sydney, Australia), Franz Schausberger, Salzburg University, Austria), Nico Steytler (University of Western Cape, South Africa), Francisco Velasco Caballero (Universidad Autonoma de Madrid, Spain), and Robert Young (University of Western Ontario, Canada)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".