Caring across Boundaries versus Keeping Boundaries Intact: Links between Moral Values and Interpersonal Orientations

<div><p>Prior work has established robust diversity in the extent to which different moral values are endorsed. Some people focus on values related to caring and fairness, whereas others assign additional moral weight to ingroup loyalty, respect for authority and established hierarchies, and purity concerns. Five studies explore associations between endorsement of distinct moral values and a suite of interpersonal orientations: Machiavellianism, prosocial resource distribution, Social Dominance Orientation, and reported likelihood of helping and not helping kin and close friends versus acquaintances and neighbors. We found that Machiavellianism (Studies 1, 3, 4, 5) (e.g., amorality, controlling and status-seeking behaviors) and Social Dominance Orientation (Study 4) were negatively associated with caring values, and positively associated with valuation of authority. Those higher in caring values were more likely to choose prosocial resource distributions (Studies 2, 3, 4) and to report reduced likelihood of failing to help kin/close friends or acquaintances (Study 4). Finally, greater likelihood of helping acquaintances was positively associated with all moral values tested <i>except</i> authority values (Study 4). The current work offers a novel approach to characterizing moral values and reveals a striking divergence between two kinds of moral values in particular: caring values and authority values. Caring values were positively linked with prosociality and negatively associated with Machiavellianism, whereas authority values were positively associated with Machiavellianism and Social Dominance Orientation.</p></div

Moral values, prosociality, and Social Dominance Orientation: Correlations across Studies 2, 3, and 4.

<p><b>Notes.</b> “Partial” refers to partial correlations with political orientation, religiosity, and gender controlled. Zero-order correlation coefficient is presented first, partial correlation coefficient is in parentheses. SDO =  Social Dominance Orientation. Boldface indicates significant correlations. <b>*</b><i>p</i><.05, <b>**</b><i>p</i><.01, <b>***</b><i>p</i><.001.</p

Summary of positive and negative correlations between moral values and prosocial and antisocial variables across all studies.

<p><b>Notes</b>. “Partial” refers to partial correlations with political orientation, religiosity, and gender controlled; (+) indicates significant positive correlation, (−) indicates significant negative correlation.</p

Moral values and Machiavellianism: Correlations across Studies 1, 3, 4, 5.

<p><b>Notes.</b> “Partial” refers to partial correlations with political orientation, religiosity, and gender controlled. Zero-order correlation coefficient is presented first, partial correlation coefficient is in parentheses. Boldface indicates significant correlations. * <i>p</i><.05, **<i>p</i><.01, ***<i>p</i><.001.</p

Results of Meta-Analyses.

<p><i>Left</i>: Illustration of results of meta-analyses of data from Studies 1, 3, 4, 5 indicating a negative relationship between Caring values and Mach Total Score, and a positive relationship between Authority values and Mach Total Score. <i>Right</i>: Illustration of results of meta-analysis of data from Studies 2, 3, 4 indicating a positive relationship between Prosociality and Caring values.</p

Summary of correlations observed across all studies.

<p>Each <i>square</i> represents an observation of a significant partial correlation (politics, religion, and gender controlled). Each <i>circle</i> represents an observation of a significant zero-order correlation. Study (#) indicated on each circle/square. Moral values are color-coded.</p

Correlations between helping task items and moral values in Study 4.

<p><b>Notes.</b> “Partial” refers to partial correlations with political orientation, religiosity, and gender controlled. Zero-order correlation coefficient is presented first, partial correlation coefficient is in parentheses. Boldface indicates significant correlations. <b>*</b><i>p</i><.05, <b>**</b><i>p</i><.01, <b>***</b><i>p</i><.001.</p

<b>NWB2023_Cocreating open science ecosystem to foster the reform of research and researchers assessment</b>

Open Science (OS) is valued in academic environments, but the actual adoption of OS practices lags behind. While some practices, such as OA publishing, have reached levels of adoption that allow them to be viewed as the standard way of producing scientific knowledge, others are not close to being mainstream. In Finland, the Ministry of Education and Culture has recognised the importance of OS for high-quality research and innovation. Therefore, the cocreation of an Open Science ecosystem in national level requires a roadmap: what services need to be in place for the ecosystem to work and what actors are to take the responsibility for providing these services. Now, Finnish Open Science and Research (OScaR) Coordination is using the enterprise architecture method for developing such a roadmap, and for instance, for identifying the essential business capabilities and services required by the open science ecosystem. The national roadmap can work as a launching pad for an international discussion on implementing OS.A shift towards a culture more favorable of OS is an interplay between multiple factors, including provision of necessary and easy-to-use infrastructure to make the OS practices feasible in the first place, normalization of OS practices, introduction of incentives for researchers to adopt OS practices, and imposition of policies to further consolidate their application. The aim of the presentation is to show the strengths and weaknesses of the OScaR architecture in developing knowledge-based management, administrative services and processes to support information systems and also practices, capabilities and indicators for responsible evaluation.</p

Addressing climate change with behavioral science: A global intervention tournament in 63 countries

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions' effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior-several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people's initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.</p

Addressing climate change with behavioral science: A global intervention tournament in 63 countries

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions’ effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior—several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people’s initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors