The market or the public domain? : global governance and the asymmetry of power
Bibliographic record
Abstract
FULL CONTRIBUTORS: Amitav Acharya, York University, UK Harry Arthurs, York University, UK Richard Devetak, Monash University, Australia Kyle Grayson, York University, UK Richard Higgott, Warwick University, UK Robin Hodess, Transparency International, Berlin Inge Kaul, United Nation's Development Program's Office of Development Studies George Kell, Executive office of the Secretary-General of the UN David Marquand, Oxford University, UK Sol Picciotto, Lancaster University, UK Simon Reich, University of Pittsburgh, USA Marcia Rioux, Robarts Centre for Canadian Studies Alasdair Roberts, Queen's University, UK John Ruggie, Columbia University, USA Keith Stewart, Toronto Environmental Alliance Geoffrey Underhill, University of Amsterdam, Netherlands Ezra Zubrow, State University of New York at Buffalo, USA
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".