Ethnic Identity in American History and America’s Exceptional Religiosity: Theory and Some Evidence
Bibliographic record
Abstract
Why is religiosity in contemporary America exceptionally high relative to those in other rich countries? I develop a simple theory that hinges on the sense of security of immigrant-identity, which is informed by both religion and ethnicity. Commitments to religion and to ethnicity are complementary in the determination of identity, and immigrants consciously invest in the endogenous component of their sense of identity through the actions they choose (like socialising with an ethnic group or performing religious activities). I demonstrate that the level of religiosity increases with the extent of ethnic fractionalisation in the society. I offer some empirical evidence for the theory using contemporary cross-sectional data from the 50 states of the US. I test this theory against two alternative theories that have been offered to explain the high American religiosity. I find a robust positive and statistically significant correlation between religiosity and state-level ethnic fractionalisation. When tested with world data, the model is rejected — lending further support for the claim that America’s religiosity derives from its unique history of exceptionally high and ethnically diverse immigration.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".