Spatial Optimism in Individuals' Future Thinking About the <scp>COVID</scp>‐19 Pandemic
Bibliographic record
Abstract
ABSTRACT Spatial optimism is the tendency to underestimate the severity of environmental threats in local relative to global contexts. We investigated whether spatial optimism was evident in people's beliefs about the estimated duration and severity of the COVID‐19 pandemic. Participants from 15 countries provided estimates of (i) when the pandemic would be brought under control and (ii) infection rates for their country and globally. Overall, individuals estimated that the pandemic would end sooner and with a lower infection rate in their own country relative to the rest of the world. This spatial optimism bias was moderated by the severity of COVID‐19 at the country level, such that the bias was greatest in countries with lower levels of pandemic severity. Findings parallel those observed for environmental threats and provide evidence for a spatial optimism bias in a distinct domain of collective thought. Implications for public‐health messaging are discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".