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Record W4288753975 · doi:10.1163/09763457-bja10013

Populist Diaspora Engagement

2022· article· en· W4288753975 on OpenAlexaff
Gözde Böcü, Nidhi Panwar

Bibliographic record

VenueDiaspora Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDiasporaOutreachTurkishPolitical scienceHindutvaPopulismIdentification (biology)Promotion (chess)Power (physics)Political economyGender studiesPublic administrationSociologyLawNationalism

Abstract

fetched live from OpenAlex

Abstract How and why do right-wing populist parties engage in diaspora outreach? This article uses populism as a lens through to study diaspora engagement, and compares strategies used by right-wing parties in power (Turkey’s AKP and India’s BJP) to access their diasporas. While we find that polarising and civilisationist discourses are adopted in both cases for uniting the diaspora behind the populist in power, we argue that these strategies are implemented for different purposes. In the Turkish case, the promotion of Turkish and Sunni-Muslim identification serves the purpose of garnering electoral support behind the ruling party, while in the Indian case, identification with Hindutva is used to achieve the financial and developmental goals of the ruling party. By comparing outreach strategies through the analysis of policies and practices employed by the parties as well as the activities of their diasporic organisations, the article contributes to debates on party-led diaspora engagement.

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)0.006
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0120.013
Scholarly communication0.0090.003
Open science0.0010.018
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.105
GPT teacher head0.379
Teacher spread0.275 · 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 designQualitative
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

Citations11
Published2022
Admission routes1
Has abstractyes

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