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 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".