Even When Ideologies Align, People Distrust Politicized Institutions
Bibliographic record
Abstract
In three studies (two preregistered; total n = 3,490 ideologically balanced U.S. adults), we examined attitudes toward 40 institutions, organizations, and groups of professionals (e.g., journalists, scientists, the Supreme Court, the World Health Organization, professors, police officers, doctors, the Catholic Church, banks, pharmaceutical companies, psychologists, Facebook), and tested the associations between (1) perceived ideological slant (the percentage of people in those institutions that lean politically left or right), (2) perceived politicization (the extent to which political values impact the work they do), and (3) public trust and willingness to support and defer to the institution’s expertise. Higher congruence between participant ideology and perceived institutional slant predicted higher trust and deference. And higher perceived politicization of institutions consistently predicted lower trust, often with large effect sizes. Similar patterns were observed between institutions, such that the institutions perceived as the most politicized were also the least trusted, with a very large effect, r = -0.76. Studies 2 and 3 found that perceived politicization also predicted lower support and willingness to defer to institutions’ expertise. Across studies, these negative relationships were observed among both participants who shared and opposed the institution’s ideological slant. In other words, even left-leaning participants were less trusting and less willing to support and defer to left-leaning institutions that appeared more politicized, and even right-leaning participants were less trusting and less willing to support and defer to right-leaning institutions that appeared more politicized. Studies 2 and 3 attempted to experimentally manipulate perceived politicization and failed to do so. We thus post this preprint in hopes of initiating discussion of these findings and identifying promising avenues for future research and possible interventions.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".