DSpace-CRIS

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Impact as equalizer: the demise of gender-related differences in anti-doping research Open PDF
(2024)
Kiss, A.
;
Soós, S.
;
Petróczi, A.
In general, the presence and performance of women in science have increased significantly in recent decades. However, gender-related differences persist and remain a global phenomenon. Women make a greater contribution to multidisciplinary research, which renders anti-doping research a compelling area for investigating the gendered aspects of academic research. The research design was based on the overall research aim to investigate whether gender in a specific field (ADS) has an effect on different aspects of research impact, including (1) the size of citation impact obtained by the research output, (2) the impact on the development of the knowledge base of ADS, expressed as the capacity of integrating knowledge from different research areas, and (3) the (expected) type of research impact targeting either societal or scientific developments (or both). We used a previously compiled dataset of 1341 scientific outputs. Using regression analysis, we explored the role of authors’ gender in citations and the effect of authorship features on scientific impact. We employed network analysis and developed a novel indicator (LinkScore) to quantify gendered authors’ knowledge integration capacity. We carried out a content analysis on a subsample of 210 outputs to explore gender differences in research goal orientation as related to gender patterns. Women’s representation has been considerably extended in the domain of ADS throughout the last two decades. On average, outputs with female corresponding authors yield a higher average citation score. Regarding women's knowledge integration roles, we can infer that no substantial gender differences can be detected. Dominantly female papers were overrepresented among publications classified as aimed at scientific progress, while the share of male-authored papers was higher in publications classified as aimed at societal progress. Although no significant gender difference was observed in knowledge integration roles, in anti-doping women appear to be more interdisciplinary than men. © The Author(s) 2024.
Gender-specific patterns in the artificial intelligence scientific ecosystem Open PDF
(2022)
Hajibabaei, A.
;
Schiffauerova, A.
;
Ebadi, A.
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 social network analysis, 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. 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. © 2022
Scientometric Analysis of Interdisciplinary Collaboration and Gender Trends in 30 Years of IEEE VIS Publications Open PDF
(2022)
Sarvghad, A.
;
Franqui-Nadal, R.
;
Reznik-Zellen, R.
;
Chawla, R.
;
Mahyar, N.
We present the results of a scientometric analysis of 30 years of IEEE VIS publications between 1990-2020, in which we conducted a multifaceted analysis of interdisciplinary collaboration and gender composition among authors. To this end, we curated BiblioVIS, a bibliometric dataset that contains rich metadata about IEEE VIS publications, including 3032 papers and 6113 authors. One of the main factors differentiating BiblioVIS from similar datasets is the authors' gender and discipline data, which we inferred through iterative rounds of computational and manual processes. Our analysis shows that, by and large, inter-institutional and interdisciplinary collaboration has been steadily growing over the past 30 years. However, interdisciplinary research was mainly between a few fields, including Computer Science, Engineering and Technology, and Medicine and Health disciplines. Our analysis of gender shows steady growth in women's authorship. Despite this growth, the gender distribution is still highly skewed, with men dominating (~75%) of this space. Our predictive analysis of gender balance shows that if the current trends continue, gender parity in the visualization field will not be reached before the third quarter of the century (~2070). Our primary goal in this work is to call the visualization community's attention to the critical topics of collaboration, diversity, and gender. Our research offers critical insights through the lens of diversity and gender to help accelerate progress towards a more diverse and representative research community. IEEE

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