Have a personal or library account? Click to login
The road to ACM fellowship: Examining collaboration patterns and disparities Cover

The road to ACM fellowship: Examining collaboration patterns and disparities

Journal of Data and Information Science
Volume 10 (2025): Issue 4 (November 2025)
By: Fan Jiang,  Tongxin Pan,  Jue Wang and  Yifang Ma  
Open Access
|Jun 2025

Figures & Tables

Figure 1.

The increasing trend of collaborating with ACM fellows by new electees, and collaboration shortens their time to ACM fellowship. (A) Percentage of new electees who have co-authored with ACM fellows each year since 1995. Trend lines fitted to data by univariate binomial regression. Group analysis by gender, country of birth, and alma mater revealed a consistent trend across all categories. (B) The violin plot represents an overall time gap of 3.8 years from graduation to ACM fellowship between the subgroup of new electees who collaborated with fellows (orange) and the subgroup of new electees who did not collaborate with fellows (green). The results of the per-decade time grouping, on the other hand, indicate a stronger association between collaboration with ACM fellows and a shorter time to fellowship among the new electees of the last ten years (a difference of 5.1 years, ***p<0.001, up from 4.0 years, ***p<0.001, and 4.3 years, ***p<0.001, in the previous twenty- and thirtyyears groups, respectively, all determined by two-sample t-tests).
The increasing trend of collaborating with ACM fellows by new electees, and collaboration shortens their time to ACM fellowship. (A) Percentage of new electees who have co-authored with ACM fellows each year since 1995. Trend lines fitted to data by univariate binomial regression. Group analysis by gender, country of birth, and alma mater revealed a consistent trend across all categories. (B) The violin plot represents an overall time gap of 3.8 years from graduation to ACM fellowship between the subgroup of new electees who collaborated with fellows (orange) and the subgroup of new electees who did not collaborate with fellows (green). The results of the per-decade time grouping, on the other hand, indicate a stronger association between collaboration with ACM fellows and a shorter time to fellowship among the new electees of the last ten years (a difference of 5.1 years, ***p<0.001, up from 4.0 years, ***p<0.001, and 4.3 years, ***p<0.001, in the previous twenty- and thirtyyears groups, respectively, all determined by two-sample t-tests).

Figure 2.

Results of a linear regression between fellow collaboration patterns (A, B), prestige of fellow collaborators (C, D), social factors (E), and time from graduation to ACM fellowship. The Y-axis indicates the time from graduation to ACM fellowship of new electees; the X-axis denotes (A) the number of collaboration times with ACM fellows; (B) the average year intervals from co-authorship papers published to elected; (C) the number of citations of the ACM fellow collaborators’ publications; (D) the number of publications of the ACM fellow collaborators. The data points are accompanied by error bars indicating the estimated 95% confidence intervals, and each subplot has a correlation coefficient indicating the strength of the linear relationship, with *** representing significance at the 1% level. (E) the error bar plot represents the coefficients of regressions of years from graduation to ACM fellowship. The independent variables are shown on the x-axis. All independent variables are binary. Focusing on all electees, fellow collaboration indicates whether electees have collaborated with fellows before receiving their fellowships, with “with fellow” coded as 1. Male electees are coded as 1 in gender; Among electees who have collaborated with fellows, the last four variables describe aspects of overlap with their fellow collaborators, including work affiliation, PhD institution, combined work/PhD institution, and subfield, with any overlap coded as 1. The pie charts illustrate the proportion of different categories divided by independent variables.
Results of a linear regression between fellow collaboration patterns (A, B), prestige of fellow collaborators (C, D), social factors (E), and time from graduation to ACM fellowship. The Y-axis indicates the time from graduation to ACM fellowship of new electees; the X-axis denotes (A) the number of collaboration times with ACM fellows; (B) the average year intervals from co-authorship papers published to elected; (C) the number of citations of the ACM fellow collaborators’ publications; (D) the number of publications of the ACM fellow collaborators. The data points are accompanied by error bars indicating the estimated 95% confidence intervals, and each subplot has a correlation coefficient indicating the strength of the linear relationship, with *** representing significance at the 1% level. (E) the error bar plot represents the coefficients of regressions of years from graduation to ACM fellowship. The independent variables are shown on the x-axis. All independent variables are binary. Focusing on all electees, fellow collaboration indicates whether electees have collaborated with fellows before receiving their fellowships, with “with fellow” coded as 1. Male electees are coded as 1 in gender; Among electees who have collaborated with fellows, the last four variables describe aspects of overlap with their fellow collaborators, including work affiliation, PhD institution, combined work/PhD institution, and subfield, with any overlap coded as 1. The pie charts illustrate the proportion of different categories divided by independent variables.
DOI: https://doi.org/10.2478/jdis-2025-0044 | Journal eISSN: 2543-683X | Journal ISSN: 2096-157X
Journal RSS Feed
Language: English
Page range: 411 - 420
Submitted on: Apr 22, 2025
|
Accepted on: Aug 13, 2025
|
Published on: Jun 22, 2025
Published by: Chinese Academy of Sciences, National Science Library
In partnership with: Paradigm Publishing Services
Keywords:
ACM fellows,
Scientific career,
Bibliometrics,
Co-authorship,
Scientific success
Related subjects:
Computer sciences,
Information technology,
Project management,
Databases and data mining

© 2025 Fan Jiang, Tongxin Pan, Jue Wang, Yifang Ma, published by Chinese Academy of Sciences, National Science Library
This work is licensed under the Creative Commons Attribution 4.0 License.

Previous articleVolume 10 (2025): Issue 4 (November 2025)