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Record W4387524898 · doi:10.1089/elj.2022.0042

Does the Framing of Information Regarding Foreign Election Interference Matter? Evidence from a Survey Experiment in Canada

2023· article· en· W4387524898 on OpenAlexaffabout
Jean‐Nicolas Bordeleau, Holly Ann Garnett

Bibliographic record

VenueElection Law Journal Rules Politics and Policy · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsRoyal Military College of CanadaEngineers Without Borders Canada
Fundersnot available
KeywordsFraming (construction)Framing effectPolitical scienceVotingGeneral electionSocial psychologyPublic relationsPsychologyPoliticsLawGeography

Abstract

fetched live from OpenAlex

As a response to foreign election interference efforts, politicians and news media have adopted various frames which highlight the hinderance of such interference in electoral processes. However, little research has examined the impact of the framing of this election interference discourse on the attitudes and behaviours of voters. This research examines the impact of negative and positive election interference frames on Canadian voters using an experimental vignette design. Specifically, this study focuses on three dependent variables: citizens' trust in electoral institutions, their likelihood of voting, and their level of comfort using alternative methods of electoral participation. The results suggest that the framing of information on the topic of election interference can have an important impact on citizens' attitudes toward the electoral process. We find that positive, but not negative, information regarding election interference influences respondents' trust in the electoral system. We also find that the effect is greatest in politically uninterested individuals. Lastly, the results show that conservatives hold more negative attitudes towards elections and voting.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.041
GPT teacher head0.343
Teacher spread0.302 · 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 teacher head, 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

Citations3
Published2023
Admission routes2
Has abstractyes

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