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Record W3012093879 · doi:10.1017/s0898588x19000166

From Personal to Partisan: Abortion, Party, and Religion Among California State Legislators

2020· article· en· W3012093879 on OpenAlexaff
David Karol, Chloe N. Thurston

Bibliographic record

VenueStudies in American Political Development · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsScience North
Fundersnot available
KeywordsAbortionPolarization (electrochemistry)LegislatorPolitical scienceLegislatureVotingPolitics of the United StatesPoliticsChristian rightState (computer science)Political economyLawSociologyLegislation

Abstract

fetched live from OpenAlex

The parties’ polarization on abortion is a signal development. Yet while the issue has been much discussed, scholars have said less about how it reveals the unstable relationship between legislators’ personal backgrounds and their issue positions. We argue that the importance of personal characteristics may wane as links between parties and interest groups develop. We focus on the case of abortion in the California State Assembly—one of the first legislative bodies to wrestle with the issue in modern times. Drawing from newly collected evidence on legislator and district religion and Assembly voting, we show that divisions on abortion were chiefly religious in the 1960s—with Catholics in both parties opposing reform—but later became highly partisan. This shift was distinct from overall polarization and was not a result of district-level factors or “sorting” of legislators by religion into parties. Instead, growing ties between new movements and parties—feminists for Democrats and the Christian Right for the Republicans—made party affiliation supplant religion as the leading cue for legislators on abortion, impelling many incumbents to revise their positions. Archival and secondary evidence further show that activists sent new cues to legislators about the importance of their positions on these issues. Showing how personal characteristics became outweighed by partisan considerations contributes to understanding of party position change and polarization, as well as processes of representation and abortion politics.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.054
GPT teacher head0.368
Teacher spread0.314 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations29
Published2020
Admission routes1
Has abstractyes

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