The DOI matches its Crossref record; 182 of the 182 references it cites were found in a registry or an index.
Navigating the social world: Toward an integrated framework for evaluating self, individuals, and groups.
Open accessAbstract
Social evaluation occurs at personal, interpersonal, group, and intergroup levels, with competing theories and evidence. Five models engage in adversarial collaboration, to identify common conceptual ground, ongoing controversies, and continuing agendas: Dual Perspective Model (Abele & Wojciszke, 2007); Behavioral Regulation Model (Leach, Ellemers, & Barreto, 2007); Dimensional Compensation Model (Yzerbyt et al., 2005); Stereotype Content Model (Fiske, Cuddy, Glick, & Xu, 2002); and Agency-Beliefs-Communion Model (Koch, Imhoff, Dotsch, Unkelbach, & Alves, 2016). Each has distinctive focus, theoretical roots, premises, and evidence. Controversies dispute dimensions: number, organization, definition, and labeling; their relative priority; and their relationship. Our first integration suggests 2 fundamental dimensions: Vertical (agency, competence, "getting ahead") and Horizontal (communion, warmth, "getting along"), with respective facets of ability and assertiveness (Vertical) and friendliness and morality (Horizontal). Depending on context, a third dimension is conservative versus progressive Beliefs. Second, different criteria for priority favor different dimensions: processing speed and subjective weight (Horizontal); pragmatic diagnosticity (Vertical); moderators include number and type of target, target-perceiver relationship, context. Finally, the relation between dimensions has similar operational moderators. As an integrative framework, the dimensions' dynamics also depend on perceiver goals (comprehension, efficiency, harmony, compatibility), each balancing top-down and bottom-up processes, for epistemic or hedonic functions. One emerging insight is that the nature and number of targets each of these models typically examines alters perceivers' evaluative goal and how bottom-up information or top-down inferences interact. This framework benefits theoretical parsimony and new research. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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Cites
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Automating Automaticity: How the Context of Human Choice Affects the Extent of Algorithmic Bias
Agan, A., Davenport, D., Ludwig, J., & Mullainathan, S. (2023) · doi:10.3386/w30981 · Cited by 25
Block, K., olsson, M. I. T., Schmader, T., van Laar, C., Martiny, S. E., Schuster, C., Van Grootel, S., Meeussen, L., & Croft, A. (2022) · doi:10.31234/osf.io/k6g5d · Cited by 7
Metadata
- Authors
- Abele, A. E., Ellemers, N., Fiske, S. T., Koch, A., & Yzerbyt, V.
- Venue
- Psychological Review
- Published
- 2021-03
- Volume
- 128
- Issue
- 2
- Pages
- 290-314
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W3087250496
- Cited by
- 380
- References
- 182
- FWCI
- 46.93
- Type
- journal article
- Language
- en
- Open access
- green
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