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Record W4398629501 · doi:10.7910/dvn/ifgsf5

Replication Data for: Who is Open to Authoritarian Governance within Western Democracies?

2020· dataset· en· W4398629501 on OpenAlexaboutno aff
Ariel Malka, Yphtach Lelkes, Bert N. Bakker, Eliyahu Spivack

Bibliographic record

VenueHarvard Dataverse · 2020
Typedataset
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsReplication (statistics)AuthoritarianismCorporate governancePolitical sciencePolitical economyDemocracySociologyLawEconomicsManagementBiologyVirology

Abstract

fetched live from OpenAlex

Recent events have raised concern about potential threats to democracy within Western countries. If Western citizens who are open to authoritarian governance share a common set of political preferences then authoritarian elites can attract mass coalitions that are willing to subvert democracy to achieve shared ideological goals. With this in mind we explored which ideological groups are most open to authoritarian governance within Western general publics using World Values Survey data from 14 Western democracies and three recent Latin American Public Opinion Project samples from Canada and the United States. Two key findings emerged. First, cultural conservatism was consistently associated with openness to authoritarian governance. Second, within half of the democracies studied, including all of the English-speaking ones, Western citizens holding a protection-based attitude package – combining cultural conservatism with left economic attitudes – were the most open to authoritarian governance. Within other countries, protection-based and consistently right-wing attitude packages were associated with similarly high levels of openness to authoritarian governance. We discuss implications for radical right populism and the possibility of splitting potentially undemocratic mass coalitions along economic lines.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.084
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0060.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.092

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 head0.115
GPT teacher head0.370
Teacher spread0.255 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2020
Admission routes1
Has abstractyes

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