Priestly Politics: Faith Formation and Political Ideology Among Catholic Clergy
Bibliographic record
Abstract
Catholic priests have long been important political actors both in mobilizing direct political action and shaping the way Catholic faithful view important public issues. Historically, Catholic clergy’s politics have been divided between more politically conservative priests who focus on “moral” issues, such as opposing same-sex marriage and abortion, and more progressive clergy who adhere to “Catholic social teachings” and advocate for antipoverty, antiracism, antiwar, and pro-immigrant initiatives. However, little research has investigated the role faith formation plays in shaping the politics of Catholic priests. We address this gap by analyzing data from a nationally representative survey of religious leaders. We find that priests who are members of religious orders are significantly more politically and theologically liberal than diocesan priests. Compared to diocesan priests, priests from religious orders are more likely to describe themselves as politically liberal, more likely to identify with the Democratic (rather than Republican) Party, more likely to endorse allowing women to have religious leadership positions, and more likely to say they would marry a same-sex couple if allowed to. These findings show that faith formation plays an important role in the politics of religious leaders and draws much needed attention to intradenominational political divisions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".