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Record W4411284103 · doi:10.1002/acp.70080

Spatial Optimism in Individuals' Future Thinking About the <scp>COVID</scp>‐19 Pandemic

2025· article· en· W4411284103 on OpenAlexaff
Sezin Öner, Karl K. Szpunar, Lynn Ann Watson, Scott Cole

Bibliographic record

VenueApplied Cognitive Psychology · 2025
Typearticle
Languageen
FieldPsychology
TopicPsychological and Temporal Perspectives Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsOptimismCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakPsychologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Social psychologyVirologyMedicineOutbreakDisease

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.675
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.060
GPT teacher head0.409
Teacher spread0.349 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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