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PreprintCited by 10

The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.

Testing Negative: The Non-Consequences of COVID-19 on Mass Ideology

Blumenau, Jack, Hicks, Timothy, Jacobs, Alan, Matthews, Scott, O'Grady, Tom·2021
DOI 10.33774/apsa-2021-qpczc
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Abstract

Responding to COVID-19, governments implemented large-scale economic and social policies of unprecedented scale. This highlighted the state's capacity to guarantee economic and health security, and affected demographic groups that are less commonly beneficiaries of state support. We hypothesise that exposure to the pandemic and these policy responses caused change in attitudes to the role of government in the economy and redistribution. We test this expectation using data from the (2014–present) British Election Study panel, together with a unique panel survey fielded to existing BES respondents in April and September, 2020. We find virtually no evidence of any effect on ideological beliefs. Moreover, using a survey experiment, we find exposure to cues linking the pandemic to greater roles for government has no impact on ideological beliefs. We conclude that such elite rhetoric, even if it had been present in the field, would not have yielded ideological change.

Overview

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Crossref · 10.33774/apsa-2021-qpczc

Cites

55 of 55 references verified0 citation errors · 0 not checkable

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Citations

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Metadata

Authors
Blumenau, J., Hicks, T., Jacobs, A., Matthews, S., & O'Grady, T.
Affiliations
  • University College London
  • University of British Columbia
  • Memorial University of Newfoundland
ORCID
Hicks, Timothy
Publisher
Cambridge University Press (CUP)
Published
2021-09-28
DOI
10.33774/apsa-2021-qpczc
Cited by
10
References
55
Type
Preprint
Licence
preprints.apsanet.org/engage/apsa/legal-information?show=terms-of-use (unspecified)

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.