Gender variations in citation distributions in medicine are very small and due to self-citation and journal prestige

Andersen, Jens Peter; Schneider, Jesper W.; Jagsi, Reshma; Nielsen, Mathias Wullum

doi:10.7554/elife.45374

Cited by 63 publications

(58 citation statements)

References 62 publications

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“…Amongst these covariates, journal impact and OA had the highest association with the number of citations and the probability of being cited, respectively. The finding about journal impact is consistent with Andersen’s et al [ 25 ] study, which found journal prestige as a covariate that accounted for most of the small average citation differences between genders. The finding about OA might show the importance of making an article open, as this makes it more visible, and thus easier for Twitter users to access the full text of articles.…”

Section: Conclusion and Discussionsupporting

confidence: 89%

“…The total numbers of publications, citations and self-citations for each author (first, last) as scientific (professional) age of an author [ 13 , 25 ] were downloaded from SciVal API using authors’ IDs in June 2020. To do this, the author IDs for first and last authors were downloaded via Scopus author API using a combination of Doi and Pubmed ID of articles.…”

Section: Methodsmentioning

confidence: 99%

“…Most of these factors have also been examined in relation to gender or altmetrics studies. In relation to citation and gender, these included factors such as journal impact [ 7 , 25 ], citations and self-citation of first and last authors [ 25 ], topic of an article (measured as MeSH) [ 8 , 26 ], number of MeSH topics [ 13 ], gender of first and last authors [ 9 ], and the total number of authors’ publications [ 13 , 27 ]. In relation to citations, these included open access (OA) [ 28 ], Mega journal [ 29 , 30 ] and number of topics of an article [ 31 ] amongst others.…”

Section: Introductionmentioning

confidence: 99%

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Could early tweet counts predict later citation counts? A gender study in Life Sciences and Biomedicine (2014–2016)

Dehdarirad

2020

PLoS ONE

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In this study, it was investigated whether early tweets counts could differentially benefit female and male (first, last) authors in terms of the later citation counts received. The data for this study comprised 47,961 articles in the research area of Life Sciences & Biomedicine from 2014–2016, retrieved from Web of Science’s Medline . For each article, the number of received citations per year was downloaded from WOS, while the number of received tweets per year was obtained from PlumX. Using the hurdle regression model, I compared the number of received citations by female and male (first, last) authored papers and then I investigated whether early tweet counts could predict the later citation counts received by female and male (first, last) authored papers. In the regression models, I controlled for several important factors that were investigated in previous research in relation to citation counts, gender or Altmetrics. These included journal impact (SNIP), number of authors, open access, research funding, topic of an article, international collaboration, lay summary, F1000 Score and mega journal. The findings showed that the percentage of papers with male authors in first or last authorship positions was higher than that for female authors. However, female first and last-authored papers had a small but significant citation advantage of 4.7% and 5.5% compared to male-authored papers. The findings also showed that irrespective of whether the factors were included in regression models or not, early tweet counts had a weak positive and significant association with the later citations counts (3.3%) and the probability of a paper being cited (21.1%). Regarding gender, the findings showed that when all variables were controlled, female (first, last) authored papers had a small citation advantage of 3.7% and 4.2% in comparison to the male authored papers for the same number of tweets.

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Section: Conclusion and Discussionsupporting

confidence: 89%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Could early tweet counts predict later citation counts? A gender study in Life Sciences and Biomedicine (2014–2016)

Dehdarirad

2020

PLoS ONE

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“…Findings regarding the average impact of publications diverge. According to some, women perform better (Symonds, Gemmell, Braisher, Gorringe, & Elgar, 2006;Duch et al, 2012), while others demonstrate the opposite (Hunter & Leahey, 2010;Larivière, Vignola-Gagné, Villeneuve, Gelinas, & Gingras, 2011;Aksnes, Rorstad, Piro, & Sivertsen, 2011) or no statistically significant gender difference (Andersen, Schneider, Jagsi, & Nielsen, 2019;Bordons, Morillo, Fernandez, & Gomez, 2003). A very recent study investigated the gender gap in citation impact for six million articles from Australia, Canada, Ireland, Jamaica, New Zealand, UK, and the USA.…”

Section: The Gender Gap In Research Performance and Its Determinantsmentioning

confidence: 99%

Gender differences in research performance within and between countries: Italy vs Norway

Abramo

Aksnes

D’Angelo

2021

Journal of Informetrics

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In this study, the scientific performance of Italian and Norwegian university professors is analysed using bibliometric indicators. The study is based on over 36,000 individuals and their publication output during the period 2011-2015. Applying a multidimensional indicator in which several aspects of the research performance are captured, we find large differences in the performance of men and women. These gender differences are evident across all analysed levels, such as country, field, and academic position. However, most of the gender differences can be explained by the tails of the distributions-in particular, there is a much higher proportion of men among the top 10% performing scientists. For the remaining 90% of the population, the gender differences are practically non-existent. The results of the two countries, which differ in terms of the societal role of women, are contrasting. Further, we discuss possible biases that are intrinsic in quantitative performance indicators, which might disfavour female researchers.

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“…Moreover, excess is not the only object of study: differences in the rate at which men and women cite themselves is another topic of recent interest (Ghiasi et al 2016;King et al 2017;Mishra et al 2018;Andersen et al 2019).…”

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confidence: 99%

How much is too much? The difference between research influence and self-citation excess

Szomszor¹,

Pendlebury²,

Adams

2020

Scientometrics

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Citations can be an indicator of publication significance, utility, attention, visibility or short-term impact but analysts need to confirm whether a high citation count for an individual is a genuine reflection of influence or a consequence of extraordinary, even excessive, self-citation. It has recently been suggested there may be increasing misrepresentation of research performance by individuals who self-cite inordinately to achieve scores and win rewards. In this paper we consider self-referencing and self-citing, describe the typical shape of self-citation patterns for carefully curated publication sets authored by 3517 Highly Cited Researchers and quantify the variance in the distribution of self-citation rates within and between all 21 Essential Science Indicators' fields. We describe both a generic level of median self-referencing rates, common to most fields, and a graphical, distribution-driven assessment of excessive self-citation that demarcates a threshold not dependent on statistical tests or percentiles (since for some fields all values are within a central 'normal' range). We describe this graphical procedure for identifying exceptional self-citation rates but emphasize the necessity for expert interpretation of the citation profiles of specific individuals, particularly in fields with atypical self-citation patterns. , for their advice and suggestions during the development of this work. We also thank an anonymous referee for their comments regarding the 'typicality' of highly-cited researchers.

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Gender variations in citation distributions in medicine are very small and due to self-citation and journal prestige

Cited by 63 publications

References 62 publications

Could early tweet counts predict later citation counts? A gender study in Life Sciences and Biomedicine (2014–2016)

Could early tweet counts predict later citation counts? A gender study in Life Sciences and Biomedicine (2014–2016)

Gender differences in research performance within and between countries: Italy vs Norway

How much is too much? The difference between research influence and self-citation excess

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