Trust in government: A micro–macro approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To date, the political trust literature has been bifurcated along micro–macro lines. Some scholars have studied differences in political trust across individuals, while others have studied aggregate political trust levels over time. In this paper, I propose a micro–macro model that joins the two. I use the model and data from the 1958–2008 American National Election Studies to examine the effects of incumbent, economic and policy assessments on individual political trust and on political trust over time. The results show that although economic and policy assessments impact individual-level political trust, they do not explain the more general trend. Assessments of incumbents, however, explain both. I argue that studies of political trust need to pay greater attention to the distinction between effect, mean and compositional changes. Only those predictors that exhibit the latter two can usefully explain why political trust changes over time. The paper concludes with a discussion of the utility of the micro–macro approach for the study of political and other forms of trust.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it