She Sees the Trees, He Sees the Forest: Descriptive Gender Stereotypes of Concreteness and Abstractness.
We utilize social role and construal-level theories to identify and explain descriptive expectations of men's and women's cognition. We find evidence of gendered construal-level stereotypes in six preregistered studies and an internal meta-analysis. First, we find that people tend to implicitly asso...
| Publicado en: | Journal of Personality & Social Psychology Vol. 129; no. 6; pp. 1054 - 1083 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
| Publicado: |
American Psychological Association
Dec2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=189683738&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 189683738 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00223514 JSS jtl: Journal of Personality & Social Psychology issn: 00223514 maglogo: N pubinfo: dt: Dec2025 vid: 129 iid: 6 pid: 34 pub: American Psychological Association artinfo: ui: 189683738 10.1037/pspa0000453 ppf: 1054 ppct: 29 formats: tig: atl: She Sees the Trees, He Sees the Forest: Descriptive Gender Stereotypes of Concreteness and Abstractness. aug: au: Dodson, Samantha J. Goodwin, Rachael D. Wakslak, Cheryl J. Diekmann, Kristina A. Graham, Jesse affil: Haskayne School of Business, University of Calgary Martin J. Whitman School of Management, Syracuse University Marshall School of Business, University of Southern California David Eccles School of Business, University of Utah su: Gender stereotypes Gender role Psychological distance Cognition Generalization sug: subj: Gender stereotypes Gender role Psychological distance Cognition Generalization keyword: construal-level theory descriptive stereotypes gender social role theory stereotype activation construal-level theory descriptive stereotypes gender social role theory stereotype activation ab: We utilize social role and construal-level theories to identify and explain descriptive expectations of men's and women's cognition. We find evidence of gendered construal-level stereotypes in six preregistered studies and an internal meta-analysis. First, we find that people tend to implicitly associate the names of women with low-construal terms and men with high-construal terms (Study 1; N = 229). In Studies 2 (N = 150), 3 (N = 601), and 4 (N = 333), we found that people tend to describe women as more concrete than men in general and across 48 occupations, although Study 4 (N = 333) added nuance to the story, finding that women were also described as more abstract than men. Across these studies, we also found that women were described as more concrete than abstract, whereas men would be described as more abstract than concrete. These stereotypic associations were observable in the language used to recommend LinkedIn users from varying industries and occupations (Study 5; N = 549,059). Study 6 reveals that beliefs that women are more concrete than men affect their assignments to desirable and undesirable detailed tasks (N = 841), a mechanism that could perpetuate gender roles and organizational inequity. The Supplemental Materials include three additional studies that help validate the present results. Finally, we conducted an internal meta-analysis (including supplemental and file drawer studies) to summarize the main effects. We discuss the theoretical implications of this research and provide recommendations for future research. Statement of Limitations: The research presented herein is limited in scope, specifically in its inability to capture longitudinal causal effects, generalize the effects to different historical periods, nationalities, and cultures beyond present-day U.S. populations, and provide convergent and divergent validity tests for some dependent measures. We also acknowledge the possibility of confounds that could not be accounted for in the archival data, the likelihood of imperfect predictions generated from machine learning algorithms, the potential for bias in researcher degrees of freedom (e.g., instrument choice) prior to preregistration, and some degree of artificiality in the experiments. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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