Triangulating the Legitimacy of International Organizations: Beliefs, Discourses, and Actions
Bibliographic record
Abstract
Abstract It is commonplace to say that international organizations (IOs) face a legitimacy crisis because they are perceived as undemocratic, unaccountable, and inefficient. Plausible as it may seem, this still must count as a conjecture. In this article, I review the rapidly growing literature that has explored this connection empirically. I follow three strands of research that approach the legitimacy of IOs from different angles. The first strand explores individual beliefs through observational and experimental surveys. The second strand analyzes public discourses, mapping arguments deployed to (de-)legitimize IOs. The third strand studies political action related to legitimacy, such as protest voting, street demonstrations, and withdrawal of member states from IOs. The empirical evidence shows that citizens expect fair procedures and balanced outcomes also in international politics; that legitimation discourses revolve around democratic standards and not only performance; and that institutions respond to protests. It is less clear, however, how the three dimensions of (de-)legitimation interact. I argue that we need to triangulate them more systematically to see the connections between beliefs, arguments, and political action at work. I suggest in-depth case studies, sensitive to the context and content of legitimation debates, that cover these three dimensions and their interactions simultaneously.
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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 | 0.045 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".