Trust repertoires and the reception of institutional responses to the COVID-19 crisis in Europe: A latent class analysis
Bibliographic record
Abstract
This article examines the role of institutional trust in current European societies. Based on a secondary data analysis of Eurobarometer data (response rate 39.6%), it maps institutional trust repertoires and analyzes their consequences for a crisis that disturbed public life immensely in 2020 and 2021—the COVID-19 pandemic and the measures to fight this. Methodologically, it applies a multilevel latent class analysis of 18 institutions. Taking inspiration from “cultural backlash” theory, the explanatory analyses incorporate socio-political values and geographical identifications. The results show that there are seven different trust repertoires in the European Union (EU) countries, ranging from 24 percent mostly trustful to 11 percent mostly distrustful. EU Countries can be clustered into four classes, each with specific repertoire distributions. Particularly satisfaction with one’s own life and world developments is associated with higher trust. Compliance with COVID-19 policies is most likely when citizens trust both national political institutions and media institutions; other institutions matter less. Country health expenditure has a limited effect on the reception of COVID-19 policies but does influence membership of trust repertoires.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".