Preprint
Emergence of Structural Inequalities in Scientific Citation Networks
ArXiv.org
03/19/2021
DOI: 10.48550/arxiv.2103.10944
Abstract
Structural inequalities persist in society, conferring systematic advantages
to some people at the expense of others, for example, by giving them
substantially more influence and opportunities. Using bibliometric data about
authors of scientific publications, we identify two types of structural
inequalities in scientific citations. First, female authors, who represent a
minority of researchers, receive less recognition for their work (through
citations) relative to male authors; second, authors affiliated with top-ranked
institutions, who are also a minority, receive substantially more recognition
compared to other authors. We present a model for the growth of directed
citation networks and show that citations disparities arise from individual
preferences to cite authors from the same group (homophily), highly cited or
active authors (preferential attachment), as well as the size of the group and
how frequently new authors join. We analyze the model and show that its
predictions align well with real-world observations. Our theoretical and
empirical analysis also suggests potential strategies to mitigate structural
inequalities in science. In particular, we find that merely increasing the
minority group size does little to narrow the disparities. Instead, reducing
the homophily of each group, frequently adding new authors to a research field
while providing them an accessible platform among existing, established
authors, together with balanced group sizes can have the largest impact on
reducing inequality. Our work highlights additional complexities of mitigating
structural disparities stemming from asymmetric relations (e.g., directed
citations) compared to symmetric relations (e.g., collaborations).
Details
- Title: Subtitle
- Emergence of Structural Inequalities in Scientific Citation Networks
- Creators
- Buddhika NettasingheNazanin AlipourfardVikram KrishnamurthyKristina Lerman
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.2103.10944
- ISSN
- 2331-8422
- Language
- English
- Date posted
- 03/19/2021
- Academic Unit
- Business Analytics
- Record Identifier
- 9984423764102771
Metrics
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