In the age of artificial intelligence, the common interest in human autonomy is experiencing a revival. Autonomy has formerly and mostly been investigated from a theoretical scientific perspective, in which scholars from various disciplines have linked autonomy with the concepts of dignity, independence from others, morality, self-awareness, and unconventionality. In a series of three semi-qualitative, preregistered online studies (total = 505), we investigated laypersons' understanding of autonomy with a bottom-up procedure to find out how far lay intuition is consistent with scientific theory. First, in Study 1, participants ( = 222) provided us with at least three and up to 10 examples of autonomous behaviors, for a total of 807 meaningful examples. With the help of blinded research assistants, we sorted the obtained examples into categories, from which we generated 34 representative items for the following studies. Next, in Study 2, we asked a new sample of participants ( = 108) to rate the degree of autonomy reflected in each of these 34 items. Last, we presented the five highest-rated and the five lowest-rated items to the participants of Study 3 ( = 175), whom we asked to evaluate how strongly they represented the components of autonomy: dignity, independence from others, morality, self-awareness, and unconventionality. We identified that dignity, independence from others, morality, and self-awareness significantly distinguished between high- and low-autonomy items, implying that high autonomy items were rated higher on dignity, independence from others, morality, and self-awareness than low autonomy items, but unconventionality did not. Our findings contribute to both our understanding of autonomous behaviors and connecting lay intuition with scientific theory.
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http://dx.doi.org/10.3389/fpsyg.2022.871797 | DOI Listing |
MDM Policy Pract
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San Francisco Division of Geriatrics, Department of Medicine, University of California, San Francisco, CA, USA.
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School of Economics and Management, Taiyuan University of Science and Technology, Taiyuan, China.
Performance management in university-based scientific research institutions is essential for driving reform, advancing education quality, and fostering innovation. However, current performance evaluation models often focus solely on research indicators, neglecting the critical interdependence between the education and research systems. This oversight leads to inefficiencies in resource allocation and an underestimation of overall institutional performance, particularly in universities with varying development levels.
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