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Gender-Specific Patterns in the Artificial Intelligence Scientific Ecosystem


Gender-Specific Patterns in the Artificial Intelligence Scientific Ecosystem

Hajibabaei, Anahita (2021) Gender-Specific Patterns in the Artificial Intelligence Scientific Ecosystem. Masters thesis, Concordia University.

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Gender disparity in science is one of the most focused debating points among authorities and the scientific community. Over the last few decades, numerous initiatives have endeavored to accelerate gender equity in academia and research society. However, despite the ongoing efforts, gaps persist across the world, and more measures need to be taken. Using various methodologies such as social network analysis, statistical analysis, bibliometrics, natural language processing, and machine learning, in this study, we comprehensively analyzed gender-specific patterns in the highly interdisciplinary and evolving field of artificial intelligence for the period of 2000-2019. This work was completed in two main phases: First, we investigated the collaboration patterns of artificial intelligence (AI) scientists to shed light on team composition characteristics in interdisciplinary research teams from a gender perspective. Next, we identified highly central AI scientists and calculated a multi-dimensional feature vector at the author level that covered multiple characteristics of scientific activities to explore the effects of driving factors on acquiring key/central network positions and explain any possible gender differences.
Our findings suggest an overall increasing rate of mixed-gender collaborations. From the observed gender-specific collaborative patterns, the existence of disciplinary homophily at both dyadic and team levels is confirmed. However, a higher preference was observed for female researchers to form homophilous collaborative links. Our core-periphery analysis indicated a significant positive association between having diverse collaboration and scientific performance and experience. We found evidence in support of expecting the rise of new female superstar researchers in the artificial intelligence field. Moreover, our findings provided a deep understanding of the profiles of highly central AI scientists and revealed that various individual author-level factors could contribute differently to occupying certain strategic network roles in the AI co-authorship network. However, some of the notable and common characteristics of central researchers, regardless of their gender, are their highly collaborative behavior and high research productivity and impact.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (Masters)
Authors:Hajibabaei, Anahita
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Quality Systems Engineering
Date:10 December 2021
Thesis Supervisor(s):Schiffauerova, Andrea and Ebadi, Ashkan
ID Code:990149
Deposited By: Anahita Hajibabaei
Deposited On:16 Jun 2022 14:41
Last Modified:16 Jun 2022 14:41
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