Bibliographic record
Abstract
The only introduction to Global Political Economy that lets students learn from the very top scholars in the field. The fifth edition of this popular text offers a comprehensive introduction to global political economy, combining theory, history, and contemporary issues and debates. Renowned for its balance of empirical material and critical analysis, the expert authors introduce readers to the diversity of perspectives in GPE, and encourage students to unpack claims and challenge explanations. This new edition features a rewritten chapter on the Global Trade Regimes and thorough updates throughout to reflect the rise of new actors and the role of developing economies in global governance. Contributors to this volume - Vinod Aggarwal, University of California, Berkeley, USA Ann Capling, University of Melbourne, Australia Peter Dauvergne, University of British Columbia, Vancouver, Canada Cedric Dupont, The Graduate Institute of International Studies, Geneva Colin Hay, University of Sheffield, UK Eric Helleiner, University of Waterloo, Canada Michael J Hiscox, Harvard University, USA Anthony McGrew, University of Strathclyde, UK Louis W Pauly, University of Toronto, Canada Nicola Phillips, University of Sheffield, UK John Ravenhill, University of Waterloo, Canada Eric Thun, Said Business School, University of Oxford, UK Silke Trommer, University of Manchester, UK Robert Hunter Wade, London School of Economics and Political Science, UK Matthew Watson, University of Warwick, UK
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.099 | 0.032 |
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".