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Cited by 217

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Stalled or Uneven Gender Revolution? A Long‐Term Processual Framework for Understanding Why Change Is Slow

Sullivan, Oriel, Gershuny, Jonathan, Robinson, John P.·2018·Journal of Family Theory & Review

Abstract

Recently much attention has been focused on whether the gender transformation of paid and unpaid work in society referred to as the gender revolution has hit a wall, or at least stalled. In this article, we discuss key trends in the gender division of labor across 13 developed countries over a 50‐year period. These trends show little decisive evidence for a stall but rather a continuing, if uneven, long‐term trend in the direction of greater gender equality. We set out a theoretical framework for understanding slow change in the division of unpaid work and care (lagged generational change). We argue that, through a long‐term view of the processes of change, this framework can help address why progress in the convergence in paid and unpaid work promised by the gender revolution has been so slow.

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Crossref · 10.1111/jftr.12248

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Metadata

Authors
Sullivan, O., Gershuny, J., & Robinson, J. P.
Venue
Journal of Family Theory & Review
Publisher
Wiley
Published
2018-02-26
Volume
10
Issue
1
Pages
263-279
ISSN
  • 1756-2570 (ISSN-L)
  • 1756-2589
OpenAlex
W2793859318
Cited by
217
References
5
FWCI
36.07
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Stalled or Uneven Gender Revolution? A Long‐Term Processual Framework for Understanding Why Change Is Slow. https://app.cochat.ai/papers/10.1111/jftr.12248
The paper
Sullivan, O., Gershuny, J., & Robinson, J. P. (2018). Stalled or Uneven Gender Revolution? A Long‐Term Processual Framework for Understanding Why Change Is Slow. Journal of Family Theory & Review. https://doi.org/10.1111/jftr.12248

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.