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Record W3024557722 · doi:10.1111/1475-6765.12401

The effect of COVID‐19 lockdowns on political support: Some good news for democracy?

2020· article· en· W3024557722 on OpenAlexafffund
Damien Bol, Marco Giani, André Blais, Peter John Loewen

Bibliographic record

VenueEuropean Journal of Political Research · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversity of TorontoUniversité de Montréal
FundersUniversity of TorontoUniversité de MontréalKing's College London
KeywordsStatus quoPoliticsCoronavirus disease 2019 (COVID-19)DemocracyPolitical scienceEnforcementGovernment (linguistics)PandemicPolitical efficacyPrime ministerSurvey data collectionPublic administrationPolitical economyLawSociologyMedicine

Abstract

fetched live from OpenAlex

Abstract Major crises can act as critical junctures or reinforce the political status quo, depending on how citizens view the performance of central institutions. We use an interrupted time series to study the political effect of the enforcement of a strict confinement policy in response to the COVID‐19 pandemic. Specifically, we take advantage of a unique representative web‐based survey that was fielded in March and April 2020 in Western Europe to compare the political support of those who took the survey right before and right after the start of the lockdown in their country. We find that lockdowns have increased vote intentions for the party of the Prime Minister/President, trust in government and satisfaction with democracy. Furthermore, we find that, while rallying individuals around current leaders and institutions, they have had no effect on traditional left–right attitudes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.185
GPT teacher head0.482
Teacher spread0.298 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations720
Published2020
Admission routes2
Has abstractyes

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