Who are the acknowledgees? An analysis of gender and academic status

Paul‐Hus, Adèle; Mongeon, Philippe; Sainte-Marie, Maxime; Larivière, Vincent

doi:10.1162/qss_a_00036

Cited by 8 publications

(6 citation statements)

References 43 publications

(54 reference statements)

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“…Nevertheless, other, less formal types of scientific collaboration deserve a closer look but typically lack data that can be utilized for respective analyses. However, recently, acknowledgments in scholarly articles that serve as a form of credit attribution were analyzed in more detail, suggesting that corresponding practices are associated with academic status and gender (Paul-Hus et al, 2020 ). More in-depth analyses would also be of interest for the field of mathematics.…”

Section: Summary and Discussionmentioning

confidence: 99%

A machine learning approach to quantify gender bias in collaboration practices of mathematicians

Steinfeldt

Mihaljević

2023

Front. Big Data

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Collaboration practices have been shown to be crucial determinants of scientific careers. We examine the effect of gender on coauthorship-based collaboration in mathematics, a discipline in which women continue to be underrepresented, especially in higher academic positions. We focus on two key aspects of scientific collaboration—the number of different coauthors and the number of single authorships. A higher number of coauthors has a positive effect on, e.g., the number of citations and productivity, while single authorships, for example, serve as evidence of scientific maturity and help to send a clear signal of one's proficiency to the community. Using machine learning-based methods, we show that collaboration networks of female mathematicians are slightly larger than those of their male colleagues when potential confounders such as seniority or total number of publications are controlled, while they author significantly fewer papers on their own. This confirms previous descriptive explorations and provides more precise models for the role of gender in collaboration in mathematics.

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Section: Summary and Discussionmentioning

confidence: 99%

A machine learning approach to quantify gender bias in collaboration practices of mathematicians

Steinfeldt

Mihaljević

2023

Front. Big Data

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“…Gender (homophily) bias is also suspected in many other realms of science, including reviews of publications, grant assignments or decisions to select co-authors or peers for acknowledgments [ 30 , 37 , 38 ]. Reliable measures of research topics (and other possible sources of gender heterogeneity) are needed not only to rule out mediators in these realms as well, but also to achieve sufficient statistical power to detect genuine gender bias that may still exist in many realms (decisions) in science [ 39 ].…”

Section: Discussionmentioning

confidence: 99%

Same-gender citations do not indicate a substantial gender homophily bias

2022

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Can the male citation advantage (more citations for papers written by male than female scientists) be explained by gender homophily bias, i.e., the preference of scientists to cite other scientists of the same gender category? Previous studies report much evidence that this is the case. However, the observed gender homophily bias may be overestimated by overlooking structural aspects such as the gender composition of research topics in which scientists specialize. When controlling for research topics at a high level of granularity, there is only little evidence for a gender homophily bias in citation decisions. Our study points out the importance of controlling structural aspects such as gendered specialization in research topics when investigating gender bias in science.

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“…We opt for the Scopus Database because it covers the main scientific databases in architectural and design research; it includes the most impactful journals in these fields (e.g. Archnet-IJAR or Design Studies) and is among the accessible databases at our institution (see Paul-Hus et al. , 2019 for a database comparative analysis).…”

Section: Methodsmentioning

confidence: 99%

Interactions between architects and end-users during housing design processes: a systematic literature review

Mertens

Hamarat

Elsen

2022

ARCH

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PurposeThis research focuses on the interactions between architects and end-users during the design process of housing projects, both experiencing challenges and friction points when meeting.Design/methodology/approachThe authors conducted a systematic literature review (SLR), based on and adapted from Kitchenham and Charters' work (2007). The thematic analysis of N = 104 identified articles reveals 13 main themes and 30 subthemes specific to architects, end-users and the interactions of architects and end-users, and 3 main groups of other actors intervening in these dynamics. The authors organize the data by actors and the actors' social encounters, themes and subthemes. The authors focus on some aspects, given possible evolution of practices.FindingsThe authors question the role of architects and the ways both parties share respective knowledge. The authors also discuss the various scales of social encounters depicted through literature, from traditional discursive meetings to participatory practices, and raise the lack of convincing tools genuinely used in current housing architecture practices. Finally, the authors point out the need for further field research in order to practically bridge the gap between researchers and practitioners.Originality/valueThe authors present an overview of the most relevant papers, organized in a table and grouped by themes. This represents a major output of this SLR, and gives the concerned readers the opportunity to get a grasp on readers' sub/theme of interest.

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Who are the acknowledgees? An analysis of gender and academic status

Cited by 8 publications

References 43 publications

A machine learning approach to quantify gender bias in collaboration practices of mathematicians

A machine learning approach to quantify gender bias in collaboration practices of mathematicians

Same-gender citations do not indicate a substantial gender homophily bias

Interactions between architects and end-users during housing design processes: a systematic literature review

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