Silences of Bretton Woods: gender inequality, racial discrimination and environmental degradation
Bibliographic record
Abstract
In the formal deliberations of the Bretton Woods conference, little was said about gender inequality, racial discrimination, or environmental degradation. At the time, however, the significance of these issues to international economic governance was prominently discussed elsewhere, including in other conferences planning the postwar international order. The fact that the Bretton Woods architects chose to ignore these issues, thus, was an anomaly that needs to be recognized as an important part of the content of the conference and its ‘embedded liberal’ normative framework. Explaining these silences also reveals important dimensions of the politics of Bretton Woods that have been understudied. More generally, this history highlights how efforts to encourage the Bretton Woods institutions to engage more with these three issues—and political resistance to those efforts—are not unique to the current era but were present at the founding of these bodies. It also contributes to recent calls for IPE scholars to devote more attention to the study of gender inequality, racial discrimination, and environmental degradation. These issues may be ‘blind spots’ in contemporary IPE, but discussions of their relevance to international economic governance have deep historical roots, including at the creation of the postwar international economic order.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.014 |
| 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".