The Effects of (De)Legitimation on Citizens’ Legitimacy Beliefs about Global Governance
Bibliographic record
Abstract
Abstract This chapter examines the potential effects of (de)legitimation on citizens’ legitimacy beliefs about global governance institutions (GGIs) through original survey experiments among the general public in ten countries worldwide: Australia, Canada, Colombia, Egypt, France, Hungary, Indonesia, Kenya, Turkey, and South Korea. Building on cueing theory, several hypotheses about the expected effects of (de)legitimation by different agents are tested. Survey respondents are exposed to different treatments of (de)legitimation by foreign ministries, citizen protests, and GGIs themselves. Focusing on the United Nations, the World Bank, and the WHO, the chapter finds that the delegitimation of GGIs by governments and citizen protests has some limited effectiveness, depending on the GGI in question. While GGI self-legitimation in itself does not boost public belief in GGIs’ legitimacy, self-legitimation is generally effective at counteracting delegitimation attempts by governments and citizen protests. Hence, GGIs are vulnerable to delegitimation by agents and actions such as hostile governments and citizen protests. Still, the experimental results demonstrate that GGIs can effectively defend themselves against such attacks and neutralize them through self-legitimation efforts. The results carry significant implications for academic research and agents in GGI legitimacy debates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".