Abortion Attitudes: An Overview of Demographic and Ideological Differences
Bibliographic record
Abstract
Despite being a defining issue in the culture war, the political psychology of abortion attitudes remains poorly understood. We address this oversight by reviewing existing literature and integrating new analyses of several large‐scale, cross‐sectional, and longitudinal datasets to identify the demographic and ideological correlates of abortion attitudes. Our review and new analyses indicate that abortion support is increasing modestly over time in both the United States and New Zealand. We also find that a plurality of respondents (43.8%) in the United States are consistently “pro‐choice,” whereas 14.8% are consistently “pro‐life,” across various elective and traumatic abortion scenarios. We then show that age, religiosity, and conservatism correlate negatively, whereas Openness to Experience correlates positively, with abortion support. New analyses of heterosexual couples further reveal that women's and men's religiosity decrease their romantic partner's abortion support. Noting inconsistent gender differences in attitudes toward abortion, we then discuss the impact of traditional gender‐role attitudes and sexism on abortion attitudes and conclude that, rather than misogyny, benevolent sexism—the belief that women should be cherished and protected—best explains opposition to abortion. Our review thus provides a comprehensive overview of the demographic and ideological variables that underly abortion attitudes and, hence, the broader culture war.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".