Heaven on Earth: Explaining religious party formation and strength
Bibliographic record
Abstract
Why do some countries have religious political parties while others do not? And why are religious parties electorally successful in some places but flop at the ballot box in others? This dissertation provides a holistic, integrated explanation of religious party development. Religious parties are formed defensively, in reaction to anticlerical campaigns by state-builders to wrest control of public services like education, poor relief, and healthcare, from religious providers. Once they are formed, the strength of religious parties is determined by the infrastructural power of the state. Weak states that fail to provide adequate public services open up space for religious communities to build a dense civil society network of private schools, hospitals, and charities. Recipients of religious largesse then support confessional parties at the ballot box. By contrast, strong states that provide efficient public services squeeze out private providers of welfare, undermining the electoral strength of religious political parties. I find strong empirical support for this argument in a statistical analysis of religious party formation and strength, using a new dataset on all religious parties that participated in national parliamentary elections between 1800 and 2015. Comparative historical analyses of Québec, Spain, Italy, France, Albania, and Turkey, also back my theoretical arguments. My findings have important implications for the literature on party formation, voting, state-building, and private welfare provision.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".