Gendered Citation Patterns across Political Science and Social Science Methodology Fields

340 indexed citations

Abstract

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About

This paper, published in 2018, received 340 indexed citations. Written by Michelle Dion, Jane L. Sumner and Sara McLaughlin Mitchell covering the research area of Gender Studies and Statistics, Probability and Uncertainty. It is primarily cited by scholars working on Gender Studies (95 citations), Statistics, Probability and Uncertainty (63 citations) and Cognitive Neuroscience (62 citations). Published in Political Analysis.

Countries where authors are citing Gendered Citation Patterns across Political Science and Social Science Methodology Fields

Specialization
Citations

This map shows the geographic impact of Gendered Citation Patterns across Political Science and Social Science Methodology Fields. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Gendered Citation Patterns across Political Science and Social Science Methodology Fields with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gendered Citation Patterns across Political Science and Social Science Methodology Fields more than expected).

Fields of papers citing Gendered Citation Patterns across Political Science and Social Science Methodology Fields

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Gendered Citation Patterns across Political Science and Social Science Methodology Fields. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Gendered Citation Patterns across Political Science and Social Science Methodology Fields.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/10.1017/pan.2018.12.

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