2010: Uncertainty and Risk and the Crisis of 2008
Bibliographic record
Abstract
We apologize at the outset for the length of this paper and offer the following reading guide. Part 1 (pp. 7-34) argues that Peter Gourevitch’s work is foundational for the open economy politics (OEP) paradigm in IPE and that neither have a well-founded position on uncertainty in economic affairs. Part 2 (pp. 34-50) discusses how the concepts risk and uncertainty were conflated in modern economic theory and why the distinction, introduced in the 1920s by JM Keynes and Frank Knight, remains relevant for decision making. Part 3 (pp.51-59) uses evidence from the financial crisis of 2008-09 to illustrate the benefits of an eclectic approach that takes seriously the roles of constitutive institutions and practices and the social devices that people rely on to make decisions in the face of uncertainty. The biblical fat seven years are over. The United States is entering hard times (Gourevitch 1986). There is no better guide for understanding this new era than to reexamine Peter Gourevitch’s land-mark book on the same subject, first published a quarter of a century ago. Throughout his career Gourevitch (2009) has always pushed for analysis that looked at the domestic rather than the international systemic determinants of politics. He does not deny the important macro-imbalances that have contributed to the financial crisis of 2008, such as the explosion of trade and budget deficits in the U.S. and China’s willingness and ability to finance them with the result of creating booming
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".