Understanding the 2007–2008 Global Financial Crisis: Lessons for Scholars of International Political Economy
Bibliographic record
Abstract
Economists have explained the 2007–2008 global financial crisis with reference to various market and regulatory failures as well as a macro-economic environment of cheap credit during the precrisis period. These developments had important political causes that scholars of international political economy (IPE) should have been well positioned to study before the crisis. How well did they anticipate the crisis? Although none foresaw all the causes, a number of IPE scholars correctly identified many of the dangers associated with new models of securitization as well as accompanying regulatory failures and the politics underlying them. IPE scholars were less successful in identifying the macroeconomic roots of the crisis, particularly the role of international capital flows in fueling the U.S. financial bubble, but some scholars did usefully explore the politics that contributed to the latter phenomenon. The study of IPE scholarship in this episode contains useful lessons for the field's future.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 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".