Bibliographic record
Abstract
Why do ethnic parties succeed in obtaining the support of members of their target ethnic group(s)? ethnic political parties now flourish across the democratic world. canada, spain, india, the united kingdom, israel, sri lanka, macedonia, south africa, and russia are only a few examples of the established or emerging democracies in which they have taken root. for social scientists interested in explaining important political phenomena, the question is worth asking for its own sake. at the same time, the answer has broader implications for those with a stake in the survival of democratic regimes. ethnic parties, and the politicization of ethnic differences more generally, are presumed to constitute a major threat to democratic stability.1 an exploration of the processes by which such parties succeed or fail, then, illuminates also the processes that undermine or preserve democracy. Drawing on a study of variation in the performance of ethnic parties in India, this book proposes a theory of ethnic party performance in one distinct family of democracies, identified here as “patronage-democracies.” Voters in patronage-democracies, I argue, choose between parties by conducting ethnic head counts rather than by comparing policy platforms or ideological positions. They formulate preferences across parties by counting the heads of co-ethnics across party personnel, preferring that party that provides greatest representation to their co-ethnics. They formulate expectations about the likely electoral outcome by counting the heads of co-ethnics across the electorate. And they vote for their preferred party only when their co-ethnics are sufficiently numerous to take it to a winning or influential position. This process of ethnic head counting is the foundation for the central argument advanced in this book: An ethnic party is likely to succeed in a patronage-democracy when it has competitive rules for intraparty advancement and when the size of the ethnic group(s) it seeks to mobilize exceeds the threshold of winning or leverage imposed by the electoral system . Competitive rules for intraparty advancement, other things equal, give a party a comparative advantage in the representation of elites from its target ethnic category. And a positive difference between the size of its target ethnic category and the threshold of winning or leverage indicates that the party has a viable shot at victory or influence.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.364 | 0.178 |
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".