Discordant benevolence: How and why people help others in the face of conflicting values

The "abortion imaginary": Shared perceptions and personal representations among everyday Americans

Drawing upon 217 in-depth interviews and the concept of the "social imaginary," we introduce the "abortion imaginary"-a set of shared understandings regarding abortion and abortion patients. We identify four interrelated facets of the U.S. abortion imaginary pertaining to who gets an abortion and why: maternal inevitability, economic decision-making, relationship precarity, and emotional fragility. We then show how shared perceptions of abortion patients diverge into polarized opinions, revealing how those who know someone who has had an abortion differ from those who do not. Centering personal "exemplars," we integrate conceptual work on social imaginaries with contact theory to illuminate how divergent opinions coexist with shared cultural understandings.

Moral foundations of pro-choice and pro-life women

Opinions on abortion are more polarized than opinions on most other moral issues. Why are some people pro-choice and some pro-life? Religious and political preferences play a role here, but pro-choice and pro-life people may also differ in other aspects. In the current preregistered study (N = 479), we investigated how pro-choice women differ in their moral foundations from pro-life women. When the Moral Foundations Questionnaire (MFQ) was applied (i.e., when declared moral principles were measured), pro-life women scored higher than pro-choice women in loyalty, authority, and purity. However, when women were asked about moral judgments indirectly via more real-life problems from the Moral Foundations Vignettes (MFV), pro-choice women scored higher than pro-life women in emotional and physical care and liberty but lower in loyalty. When we additionally controlled for religious practice and political views, we found no differences between groups in declaring moral foundations (MFQ). However, in the case of real-life moral judgments (MFV), we observed higher care, fairness, and liberty among pro-choice and higher authority and purity among pro-life. Our results show intriguing nuances between women pro-choice and pro-life as we found a different pattern of moral foundations in those groups depending on whether we measured their declared abstract moral principles or moral judgment about real-life situations. We also showed how religious practice and political views might play a role in such differences. We conclude that attitudes to abortion "go beyond" abstract moral principles, and the real-life context matters in moral judgments.

Graphical abstract

Supplementary information

The online version contains supplementary material available at 10.1007/s12144-023-04800-0.

A Theory of Information Compression: When Judgments Are Costly

A Theory of Information Compression: When Judgments Are Costly

How useful to tourists are thousands of reviews of different five-star hotels in a city on a travel website when the mean rating is 4.5, and all the five-star hotels score around the mean? How insightful are reviews of physicians on a physician review website to potential patients when the ratings cluster tightly around an average for all physicians? Are there costs to the physicians, the patients, and to society as a whole? When all the students at a university score “A” grades on most courses, are there consequences for the university, the students, and potential employers? This paper calls the “clustering around a mean” phenomenon “information compression” and the systems in which it occurs (e.g., universities, students, employers) “judgment networks.” When there is extensive information compression in a system, measures such as ratings or grades have little value for decision makers. When all five-star hotels in a city score an average of 4.5 does it really matter which one a traveler chooses? The paper introduces a way of measuring information compression. It also suggests ways for organizations to overcome the negative consequences of information compression for themselves and their various stakeholders.