IO Legitimacy and Descriptive Representation
Bibliographic record
Abstract
Under what conditions do citizens view international organizations (IOs) as legitimate? While past work has investigated the impact of procedural and substantive dimensions on the legitimacy of IOs, as well as the role of elite communications, the effect of descriptive gender representation in IOs remains largely unaddressed. Drawing from research on descriptive representation and political institutions in domestic settings, we identify how the gendered construction of policy domains and individual attitudes toward gender equality can shape the impact of female representation. First, we argue that greater gender representation increases the legitimacy of the IO when the IO governs a stereotypically “feminine” (children’s health) rather than “masculine” (international trade) policy domain. Second, we argue this relationship is contingent on attitudes toward gender equality more broadly. We test our expectations using an online, representative survey in Canada (N=2000) using a 2x2 factorial experiment and conjoint experiment. This project thus advances the study of gender and representation in global governance and our understanding of the popular sources of IO legitimacy.
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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 | 0.011 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".