Finding critical trusters: A response pattern model of political trust
Bibliographic record
Abstract
How can declining political trust in Western democracies be explained, especially, when it remains stable and high in authoritarian societies? Underlying this question is a debate about whether political trust represents a diffuse orientation toward the political system as a whole or a specific assessment of incumbent performance. This article argues that the solution requires a move away from existing approaches that focus on question content and instead thinking about the pattern of responses. While previous work assumes that individuals display both specific and diffuse trust, we argue that the individual patterning of responses indicates either diffuse or specific trust but not both. We develop a response pattern model and use it to identify three types of individuals – critical trusters (specific trust), compliants (diffuse trust), and cynics (diffuse distrust). Tests of the model with the World Values Survey (WVS) and the US General Social Survey (GSS) show that democracies have a higher proportion of critical trusters than other systems of government and that the proportion of critical trusters has increased over time in the United States. The response pattern model directly connects cross-national and longitudinal empirical evidence to theory about the relationship between democracy and different types of trust.
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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 | 0.020 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".