Emergence of Structural Inequalities in Scientific Citation Networks

• URL: http://arxiv.org/abs/2103.10944v2
• Date: Sun, 2 May 2021 02:52:08 GMT
• Title: Emergence of Structural Inequalities in Scientific Citation Networks
• Authors: Buddhika Nettasinghe, Nazanin Alipourfard, Vikram Krishnamurthy, Kristina Lerman
• Abstract summary: 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 relative to male authors. Second, authors affiliated with top-ranked institutions, who are also a minority, receive substantially more recognition compared to other authors.
• Score: 20.754274052686355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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).

Related papers

• Biases in gendered citation practices: an exploratory study and some reflections on the Matthew and Matilda effects [2.0277446818410994]
  This paper aims at analyzing gendered citation practices in the software engineering literature. Our results show that some efforts still need to be done to achieve fairness in citation practices in the SE field.
  arXiv  Detail & Related papers  (2024-09-18T19:53:41Z)
• Inclusivity in Large Language Models: Personality Traits and Gender Bias in Scientific Abstracts [49.97673761305336]
  We evaluate three large language models (LLMs) for their alignment with human narrative styles and potential gender biases. Our findings indicate that, while these models generally produce text closely resembling human authored content, variations in stylistic features suggest significant gender biases.
  arXiv  Detail & Related papers  (2024-06-27T19:26:11Z)
• The Howard-Harvard effect: Institutional reproduction of intersectional inequalities [0.36868085124383626]
  The U.S. higher education system concentrates the production of science and scientists within a few institutions. This has implications for minoritized scholars and the topics with which they are disproportionately associated. We observe a Howard-Harvard effect, in which the topical profile of minoritized scholars are amplified in mission-driven institutions.
  arXiv  Detail & Related papers  (2024-02-06T20:46:54Z)
• Fusion of the Power from Citations: Enhance your Influence by Integrating Information from References [3.607567777043649]
  This study aims to formulate the prediction problem to identify whether one paper can increase scholars' influence or not. By applying the framework in this work, scholars can identify whether their papers can improve their influence in the future.
  arXiv  Detail & Related papers  (2023-10-27T19:51:44Z)
• Are Commercial Face Detection Models as Biased as Academic Models? [64.71318433419636]
  We compare academic and commercial face detection systems, specifically examining robustness to noise. We find that state-of-the-art academic face detection models exhibit demographic disparities in their noise robustness. We conclude that commercial models are always as biased or more biased than an academic model.
  arXiv  Detail & Related papers  (2022-01-25T02:21:42Z)
• Statistical discrimination in learning agents [64.78141757063142]
  Statistical discrimination emerges in agent policies as a function of both the bias in the training population and of agent architecture. We show that less discrimination emerges with agents that use recurrent neural networks, and when their training environment has less bias.
  arXiv  Detail & Related papers  (2021-10-21T18:28:57Z)
• Author Clustering and Topic Estimation for Short Texts [69.54017251622211]
  We propose a novel model that expands on the Latent Dirichlet Allocation by modeling strong dependence among the words in the same document. We also simultaneously cluster users, removing the need for post-hoc cluster estimation. Our method performs as well as -- or better -- than traditional approaches to problems arising in short text.
  arXiv  Detail & Related papers  (2021-06-15T20:55:55Z)
• Enhancing Scientific Papers Summarization with Citation Graph [78.65955304229863]
  We redefine the task of scientific papers summarization by utilizing their citation graph. We construct a novel scientific papers summarization dataset Semantic Scholar Network (SSN) which contains 141K research papers in different domains. Our model can achieve competitive performance when compared with the pretrained models.
  arXiv  Detail & Related papers  (2021-04-07T11:13:35Z)
• Early Indicators of Scientific Impact: Predicting Citations with Altmetrics [0.0]
  We use altmetrics to predict the short-term and long-term citations that a scholarly publication could receive. We build various classification and regression models and evaluate their performance, finding neural networks and ensemble models to perform best for these tasks.
  arXiv  Detail & Related papers  (2020-12-25T16:25:07Z)
• What's New? Summarizing Contributions in Scientific Literature [85.95906677964815]
  We introduce a new task of disentangled paper summarization, which seeks to generate separate summaries for the paper contributions and the context of the work. We extend the S2ORC corpus of academic articles by adding disentangled "contribution" and "context" reference labels. We propose a comprehensive automatic evaluation protocol which reports the relevance, novelty, and disentanglement of generated outputs.
  arXiv  Detail & Related papers  (2020-11-06T02:23:01Z)
• The Demise of Single-Authored Publications in Computer Science: A Citation Network Analysis [0.0]
  I analyze the DBLP database to study role of single author publications in the computer science literature between 1940 and 2019. I examine the demographics and reception by computing the population fraction, citation statistics, and scores of single author publications over the years.
  arXiv  Detail & Related papers  (2020-01-02T07:47:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.