Bibliographic record
Abstract
Citation (2016), "Editorial Advisory Board", Analytical Gains of Geopolitical Economy (Research in Political Economy, Vol. 30B), Emerald Group Publishing Limited, Bingley, p. vii. https://doi.org/10.1108/S0161-72302015000030B011 Publisher: Emerald Group Publishing Limited Copyright © 2016 Emerald Group Publishing Limited GENERAL EDITOR Paul Zarembka State University of New York at Buffalo, USA EDITORIAL BOARD Radhika Desai University of Manitoba, Canada Juanita Elias University of Warwick, UK Thomas Ferguson University of Massachusetts at Boston, USA Seongjin Jeong Gyeongsang National University, South Korea Jie Meng Tsinghua University, People’s Republic of China Ozgur Orhangazi Kadir Has University, Turkey Paul Cooney Seisdedos Universidade Federal do Pará, Brazil Susanne Soederberg Queens University, Canada Jan Toporowski The School of Oriental and African Studies, University of London, UK Book Chapters Analytical Gains of Geopolitical Economy Research in Political Economy Analytical Gains of Geopolitical Economy Copyright Page Editorial Advisory Board List of Contributors Introduction: Putting Geopolitical Economy to Work Part I: The International Monetary System The Inherent Instability of National Monetary Power in the 21st Century: The Triffin Dilemma Revisited The Currency Hierarchy in Center-Periphery Relationships Quasi-World Money and International Reserves Part II: World Trade and Investment Uneven and Combined Development in the Doha Stalemate China’s “South-South” Trade: Unequal Exchange and Uneven and Combined Development The New Scramble for Africa: BRICS Strategies in a Multipolar World Part III: The Persistence of Unevenness Argentine Industrialization: A Critique of the Liberal and Dependentist Schools EU Integration as Uneven and Combined Development
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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 | 0.013 | 0.073 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.370 | 0.321 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".