Exploring the Process of Policy Overreaction: The COVID-19 Lockdown Decisions
Bibliographic record
Abstract
Policy overreaction is a common phenomenon, especially in complex and emergency situations where politicians are led to make decisions fast. In these emergency decisions, emotions run generally high and cognitive processes are often impaired. The conditions of policy overreaction are in place as emotions overwhelm decision makers' rational processes. Drawing on the response patterns of three countries to the COVID-19 pandemic, we develop a process model of policy overreaction which describes the effects of negative emotions and institutional isomorphism on policy decision-making. Our model highlights four critical stages: negative emotions buildup, propagation of fear, isomorphic decision-making, and leading to an intractable crisis. This article shows precisely how the cascading effect of negative emotions, particularly fear, is contagious and spreads to generate crowd effects, which bend considerably policy makers' ability to make rational decisions. Our theory provides a better understanding of the process by which policy overreaction takes place.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".