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Record W3115184962 · doi:10.1111/jcms.13143

Populist Radical Right Parties in Europe: What Impact Do they Have on Development Policy?

2020· article· en· W3115184962 on OpenAlexaff
Julian Bergmann, Christine Hackenesch, Daniel Stockemer

Bibliographic record

VenueJCMS Journal of Common Market Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPopulism, Right-Wing Movements
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFraming (construction)Opposition (politics)Political scienceEuropean unionSalience (neuroscience)Political economyEconomicsEconomic policyPoliticsLawGeography

Abstract

fetched live from OpenAlex

Abstract Previous research suggests that the rise of populist radical right parties (PRRPs) is contributing to the politicization of European domestic and external policies. However, whether this is also the case for European development policy is unclear. Building on a new dataset that analyses government positions and coalition agreements across European countries since the 1990s, we investigate whether, and if so how, the strength of PRRPs affects European governments' framing of the relationship between migration and development policy. Research on PRRPs suggests that they influence other parties' positions directly when they are in government, or indirectly by framing topics such as migration differently from other parties, thereby pushing government and opposition parties to modify their own positions. We find (moderate) support for PRRPs' indirect influence on the framing and salience of the migration–development policy nexus, via their vote and seat share. The effect of PRRPs in government on the formulation of development aid policy goals is smaller.

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)0.008
metaresearch head score (Gemma)0.023
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0020.004
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.067
GPT teacher head0.391
Teacher spread0.324 · 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

Citations44
Published2020
Admission routes1
Has abstractyes

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