Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".