Ambiguity and Uncertainty in International Organizations: A History of Debating IMF Conditionality<sup>1</sup>
Bibliographic record
Abstract
How do international organizations deal with the persistent challenge of uncertainty? The most intuitive answer is through regulation. Yet, rules are not always the best solution in times of uncertainty or in dealing with complex and diverse problems. More ambiguous policies that leave room for interpretation, can often be more functional for an international organization (IO); moreover, ambiguities can also be a source of power—and are therefore often a subject of conflict among institutional actors. Focusing on the case of International Monetary Fund conditionality policy, this article provides several key insights into IO practices. It provides an account of the different forms that ambiguity can take in international organizations and develops an explanation for why institutional ambiguities appear and persist. Looking inside the IO black box, the study examines how interests, institutional culture, and legitimacy concerns shape actors’ support for ambiguity, and how these preferences combine with broader structural factors to produce a predisposition toward institutional ambiguity. Finally, this article points toward certain implications of organizations’ tendency toward ambiguity, suggesting that this may play an important role in enabling institutional expansion.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.050 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.008 | 0.009 |
| 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".