Public Accountability beyond Institutional Control. Who do Citizens Blame for Failures in Governing Systemic Risk?
Bibliographic record
Abstract
Why are public institutions blamed for crises caused by private actors’ mismanagement? Building on accountability theory, this study investigates the mechanisms of blame attribution during crises, when public institutions fail to prevent systemic risks caused by private sector agents. Data from a survey experiment with 1,626 Swiss citizens reveals how perceived situational control, causal responsibility, normative accountability expectations, and accountability performance shape citizens’ attribution of blame. The findings reveal asymmetries in the responsibility-accountability logic, showing that citizens hold public institutions accountable for private sector failures if they strongly believe that public institutions had causal responsibility and situational control over the crisis. Citizens’ normative expectations of public institutions’ obligation to govern systemic risks are strongly correlated with blame, while public institutions’ accountability performance is not associated with citizens’ blame attribution. These novel findings provide critical insights for policy makers to manage perceptions, maintain trust, and safeguard legitimacy during crises. The study advances accountability theory by highlighting the dynamic nature of blame, which is particularly relevant for crisis governance.
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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 | 0.036 |
| 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 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".