Blind spots in IPE: marginalized perspectives and neglected trends in contemporary capitalism
Bibliographic record
Abstract
Which blind spots shape scholarship in International Political Economy (IPE)? That question animates the contributions to a double special issue—one in the Review of International Political Economy, and a companion one in New Political Economy. The global financial crisis had seemed to vindicate broad-ranging IPE perspectives at the expense of narrow economics theories. Yet the tumultuous decade since then has confronted IPE scholars with rapidly-shifting global dynamics, many of which had remained underappreciated. We use the Blind Spots moniker in an attempt to push the topics covered here higher up the scholarly agenda—issues that range from institutionalized racism and misogyny to the rise of big tech, intensifying corporate power, expertise-dynamics in global governance, assetization, and climate change. Gendered and racial inequalities as blind spots have a particular charge. There has been a self-reinforcing correspondence between topics that have counted as important, people to whom they matter personally, and the latter’s ability to build careers on them. In that sense, our mission is not only to highlight collective blind spots that may dull IPE’s capacity to theorize the current moment. It is also a normative one—a form of disciplinary housekeeping to help correct both intellectual and professional entrenched biases.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.053 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".