Constructing the International Economy
Bibliographic record
Abstract
Introduction: Constructing the International Economy by Rawi Abdelal, Mark Blyth, and Craig ParsonsPART I. MEANING1. Shrinking the State: Neoliberal Economists and Spending in Latin America by Jeffrey M. Chwieroth2. The Meaning Development: Constructing the World Bank's Good Governance Agenda by Catherine Weaver3. Institutionalized Hypocrisy and the Politics Agricultural Trade by Mlada BukovanskyPART II. COGNITION4. Frames, Scripts, and the Making Regional Trade Areas by Francesco Duina5. Imagined Economies: Constructivist Political Economy, Nationalism, and Economic-based Sovereignty Movements in Russia by Yoshiko M. HerreraPART III. UNCERTAINTY6. Firm Interests in Uncertain Times: Business Lobbying in Multilateral Service Liberalization by Cornelia Woll7. Trade-offs and Trinities: Forces and Monetary Cooperation by Wesley W. WidmaierPART IV. SUBJECTIVITY8. Moby Dick or Moby Doll? Discourse, or How to Study the Social Construction of All the Way Down by Charlotte Epstein9. Bringing Power Back In: The IMF's Constructivist Strategy in Critical Perspective by Jacqueline Best10. The Ethical Investor, Embodied Economies, and International Political Economy by Paul LangleyRe-constructing IPE: Some Conclusions Drawn from a Crisis by Rawi Abdelal, Mark Blyth, and Craig ParsonsReferences Index
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".