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Record W4410969589 · doi:10.1080/00380237.2025.2511609

Priestly Politics: Faith Formation and Political Ideology Among Catholic Clergy

2025· article· en· W4410969589 on OpenAlexaff
Kathleen M. Sellers, Joseph Roso

Bibliographic record

VenueSociological Focus · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicReligion and Society Interactions
Canadian institutionsAmbrose University
Fundersnot available
KeywordsPoliticsFaithIdeologySociologyReligious studiesPolitical scienceGender studiesTheologyPhilosophyLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.347
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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