Public Support for International Human Rights Institutions: A Cross‐National and Multilevel Analysis
Bibliographic record
Abstract
The expansion of international human rights institutions has drawn much attention. Bringing together theories from sociology, political science, and international law, this article examines what factors promote public support for international human rights institutions, using the recent wave of the World Values Survey data (2005–2008). The level of public support displays both cross‐national and cross‐individual variations, so I conceptualize it as a two‐level process and employ the multilevel modeling. At the individual level, it is found that men, younger people, and individuals with more education and income show a higher level of support. At the country level, national affluence, political change (de‐democratization), and linkage to the world society are associated with more support. I further integrate individual‐level characteristics and country‐level social contexts, and pay special attention to education. Education is the institutional link between macro‐level social influences and micro‐level individual attitudes. I find that the support‐promoting effect of education is contingent on social contexts. It is more salient in wealthy countries and countries with strong ties to the world society.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".