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Edge interventions can mitigate demographic and prestige disparities in the Computer Science coauthorship network

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A single coauthorship edge can boost a scholar's centrality and predicted job rank.

desk verdict A genuinely useful empirical paper on CS coauthorship disparities, with a solid structural centrality result and a placement-gain claim that is softer than the abstract suggests. read the letter →

arxiv 2506.04435 v2 pith:IV2RH2MJ submitted 2025-06-04 physics.soc-ph cs.CYcs.SI

Kate Barnes , Mia Ellis-Einhorn , Carolina Chávez-Ruelas , Nayera Hasan , Mohammad Fanous , Blair D. Sullivan , Sorelle Friedler , Aaron Clauset This is my paper
classification physics.soc-phcs.CYcs.SI
keywords networkfairnessedgeinterventionsdemographicinferencescienceofcoauthorshipnetworksclosenesscentralityinstitutionalprestigecomputerfacultycensus
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that the disparities visible in the computer science coauthorship network—women and racially minoritized scholars sit closer to the periphery, and faculty at lower-ranked universities are far less central—can be reduced by a single simulated edge intervention: matching a target scholar with a coauthor from a highly ranked institution. Using a hand-collected census of 5,670 U.S. computer science faculty and their DBLP coauthorship records, the authors show that closeness centrality is strongly correlated with institutional prestige, and that women and racially minoritized scholars are significantly less central even though their institutions are not lower-ranked. They then simulate adding one edge linking each target to a randomly chosen sponsor from a top-10 or top-20 ranked department, without using any knowledge of the network structure. The intervention raises every target's closeness centrality, with larger gains for scholars at lower-ranked institutions, and when applied during a scholar's Ph.D. period it improves the rank of their predicted faculty placement. If the placement model is right, targeted facilitation of early-career collaborations could be a lightweight policy lever for reducing both prestige and demographic inequity.

What carries the argument

The load-bearing mechanism is the edge intervention, with institutional prestige standing in for network centrality. The intervention algorithm takes a target defined only by minoritized race identity and low institutional rank, draws a random sponsor from a top-10 or top-20 ranked department, and adds a single edge to the coauthorship network, interpreted as a facilitated collaboration. Because closeness centrality $C(v_i)$, the inverse of the mean shortest-path distance from node $v_i$ to all other nodes, is strongly correlated with institutional prestige, connecting a peripheral target to a central sponsor shortcuts many of the target's paths into the rest of the network. The demographic meta-labeling algorithms, which combine name-based inference with perception and are calibrated to maximize agreement with 820 survey self-reports, supply the demographic labels that define target populations, and the placement model M1, a linear regression of current institution rank on Ph.D. rank, degree, and closeness centrality, converts the centrality gain into a predicted job-market improvement. The algorithm is deliberately network-blind: sponsors are chosen by institutional rank alone, which is what makes the intervention feasible to implement as a fellowship or sponsored-collaboration program.

What would settle it

Re-estimate the placement model M1 using Ph.D. centrality computed only from coauthorships that predate each scholar's first faculty job (or Ph.D. defense) rather than the first-publication-year-plus-five window; if the closeness coefficient vanishes or reverses, the simulated improvement in placement rank is an artifact of post-placement collaborations. A second, more direct check is to implement the sponsored-collaboration program with random assignment and compare actual placement ranks of treated and control scholars.

Watch

Extended reading notes

Core claim

The central discovery is that this structural inequity is not fixed: it responds to a minimal, realistic intervention. In the cumulative network, closeness centrality tracks institutional placement-power rank (the authors report a linear relationship with $R^2 = -0.81$), and women and racially minoritized scholars have significantly lower closeness centrality than men and majority-group scholars. The intervention selects targets only by minoritized race and low institutional rank—not by network position—and sponsors only by high institutional rank, then adds one new coauthorship edge. This single edge increases the target's closeness centrality in every case, with proportionally larger improvements at lower-ranked institutions, and the effect persists when the same procedure is applied to Ph.D.-period networks built from the first five years of a scholar's publication record. Feeding the post-intervention centrality through the paper's placement model M1, the predicted rank of the target's hiring institution improves as well. The authors are explicit that this does not establish causality; it establishes that a network-blind, prestige-based collaboration policy could plausibly mitigate the measured disparities.

Load-bearing premise

The load-bearing assumption is that a scholar's coauthorship network in the first five years after their first publication accurately represents their position when they entered the faculty job market; if collaborations formed after placement are counted, the estimated effect of early-career centrality on hiring rank, and hence the intervention's predicted benefit, would be overstated.

Editorial extensions

If this is right

  • If the placement model is correct, a single facilitated collaboration during a Ph.D. improves not only centrality but the predicted rank of the institution where the scholar is hired, and the estimated improvement is largest for scholars from the lowest-ranked Ph.D. institutions.
  • Because targets and sponsors are chosen without looking at the coauthorship network, the intervention is implementable as a targeted fellowship program that uses only institutional prestige and demographic data.
  • The intervention raises closeness centrality for every target, while a greedy network-aware sponsor choice gives an upper bound; the gap between the network-blind and greedy versions quantifies how much additional gain requires network information.
  • The strong core-periphery correlation between centrality and prestige implies that coauthorship structure itself amplifies prestige inequalities in the spread of scientific ideas, extending the earlier finding that prestige drives epistemic inequality.
  • Because real computer science papers typically have more than two authors, an actual sponsored collaboration would add several edges, so the simulated single-edge improvement is a conservative lower bound on the effect.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: if the prestige-centrality correlation holds within subfields or research areas, the same network-blind targeting could be applied inside subcommunities rather than across whole departments; the census data do not resolve this.
  • Inference: a natural field test would randomize a sponsored-collaboration fellowship and compare treated scholars against matched controls on the paper's own outcomes, and the effect sizes reported in the paper are concrete enough to power such a study.
  • Inference: the paper's negative result on gendered homophily, which contrasts with earlier studies, suggests that measuring homophily while holding the network structure fixed changes the conclusion; this measurement contrast likely carries over to other collaboration networks.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. This paper presents a hand-collected census of 5,670 tenured and tenure-track computer science faculty at 178 U.S. Ph.D.-granting departments, linked to DBLP coauthorship data. The authors introduce meta-labeling algorithms that combine name-based inference and perception-based labels to infer gender and race, calibrated against a 15% self-reported survey (820 responses). They document a strong correlation between institutional prestige and network centrality and find that women and minoritized-race scholars have lower closeness centrality. They then simulate single-edge interventions that connect targets (minoritized-race faculty at lower-ranked institutions) to sponsors at top-10 or top-20 institutions, showing that these interventions increase the target's closeness centrality and, when applied to Ph.D.-period networks, improve the predicted rank of the placement institution according to regression model M1.

Significance. The main strengths of the paper are the public release of a hand-curated census-scale dataset, the cross-validated meta-labeling methodology, and the demonstration, via permutation tests and direct network computation, that prestige and demographic disparities in coauthorship centrality exist and that a minimal, network-agnostic edge addition reliably increases target centrality. The centrality-intervention result is robust and is a genuinely useful contribution to the algorithmic-fairness and science-of-science literatures. However, the placement-prediction component is not yet supported: the predicted rank gains are generated by feeding post-intervention centrality into a model that omits coauthor prestige (the very thing the intervention changes), and the Ph.D.-period network window may include post-placement collaborations. With appropriate re-specification and sensitivity analyses, the paper could be a strong contribution; in its current form, the headline placement claim needs substantial work.

major comments (3)
  1. [§6.2–§6.3] The claim in the Abstract and Section 6.3 that the intervention 'improves the predicted rank of their placement institution' rests on applying M1 with post-intervention closeness centrality. M1 predicts current institution rank from Ph.D. institution rank and Ph.D. closeness centrality alone, but the intervention also adds an edge to a coauthor at a top-10 or top-20 institution, creating a coauthor-prestige channel (letters, visibility, access to search committees) that is omitted from the model. The closeness coefficient in M1 therefore absorbs any direct effect of sponsor prestige on placement, and the predicted improvement is a within-sample extrapolation, not a valid counterfactual. I recommend re-estimating M1 with the sponsor's institution rank (or the coauthor's closeness or prestige) as a control, and reporting whether the predicted placement gain survives; if it does not, the placement claim should be removed or substantially qualified.
  2. [§4.3 and §6.2] The Ph.D. network is built from all DBLP publications with publication year at most t1+5, where t1 is the year of first publication (Section 4.3). For scholars who enter the faculty job market within three to five years of t1, this window includes collaborations formed after placement, so the 'Ph.D. centrality' used in M1 and in the simulated intervention is contaminated by the outcome it is supposed to predict. This biases both the M1 coefficient and the counterfactual placement gains. Please run the analysis with an earlier cutoff (for example, t1+3 or the actual Ph.D. year when available) and show that the results are stable, or justify the five-year window with placement-timing data.
  3. [§3.4 and §5.3] The demographic labels used for the disparity analysis and for target selection in the interventions have known, documented errors: Section 3.4.1 states that the meta-labeler 'often misidentif[ies] Middle Eastern / North African or Black names as White', and Section 3.4.2 reports that all 15 self-reported non-binary scholars are mislabeled. Because the intervention targets 'minoritized race' faculty, misclassification directly affects which individuals are selected and could bias the reported centrality gains. I request sensitivity analyses that (i) restrict the disparity and intervention analyses to survey respondents or to high-confidence labels, and (ii) quantify the expected misclassification rate per target bin and its effect on the estimated centrality improvements.
minor comments (6)
  1. [§5.1] The text reports 'R2 = −0.81' for the linear relationship between closeness centrality and prestige rank; an R2 value cannot be negative. If the authors mean a Pearson correlation of r = −0.81, they should write that, or report R2 = 0.66.
  2. [§3.4.1] It would help readers to report the final chosen race ordering's validation accuracy on the survey data in the main text, rather than only in Appendix A.4, since 137 orderings tied at 92% and the chosen one is justified by a preference to reduce false White labels.
  3. [§4.2] Please clarify in the main text why the cumulative network has 5,348 nodes rather than 5,670; while the 323 excluded faculty are mentioned, making the denominator explicit in the same paragraph would prevent misreading.
  4. [§3.1] The phrase 'complete census' is qualified by the error analysis (11/400 ineligible titles); consider reporting a net sample size after excluding those ineligible cases.
  5. [Figures 4 and 6] The y-axis label 'Proportion improvement closeness' and the text's 'proportional improvement' should be harmonized to 'proportional increase in closeness centrality' for clarity.
  6. [§6.1] The manuscript alternates between 'minority race' and 'minoritized race'; since the authors define 'minoritized' to refer to groups making up under 20% of the population, the term should be used consistently throughout.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the centrality intervention is a direct network computation and demographic labels are externally calibrated; the placement-gain claim is an explicitly disclosed model-based counterfactual.

full rationale

The paper's central results do not reduce to their own inputs. The demographic meta-labeling algorithms are optimized and cross-validated against an external survey of self-identified race and gender, so the downstream disparity analyses use externally calibrated labels rather than fitting the outcome of interest. The centrality intervention in Sections 6.1 and 6.3 is evaluated by recomputing closeness centrality after adding a single edge to a sponsor from a top-ranked institution; this is a direct graph-theoretic computation independent of any fitted model, and targets and sponsors are selected using only institutional rank and demographic group, not the network structure. The only potentially questionable step is the Ph.D. placement improvement in Section 6.3, where post-intervention closeness is fed into the same M1 regression fitted in Section 6.2, making the predicted rank gain a within-model counterfactual rather than an independent empirical validation. However, the paper explicitly labels these outputs as 'estimated' and 'predicted,' and it disclaims causal interpretation both in Section 6.3 ('our results are not causal') and in Section 7 ('our intervention experiment on Ph.D. placement was based on a causal assumption between centrality and job placement'), so this is a disclosed modeling limitation rather than a hidden circular derivation. The self-citations, including the use of the Wapman et al. placement-power measure, are external published results that are corroborated by CSRankings and USNWR analyses in the appendix, so they are not load-bearing in a circular way. Overall, no load-bearing claim is equivalent by construction to its inputs or to an unverified self-citation.

Assumptions & free parameters 6 free parameters · 7 assumptions · 0 invented entities

The central claims rest on multiple fitted parameters (meta-labeler thresholds, regression coefficients, intervention design choices) and on domain assumptions about DBLP completeness, representativeness of the survey, and the timing of the Ph.D. network. No new physical or theoretical entities are introduced.

free parameters (6)
  • Race meta-labeler thresholds and category ordering = Various thresholds (e.g., Ethnea Southeast Asian p>=0.90, EthnicolrWiki White p>=0.74) and ordering (Southeast Asian…
    Chosen by brute-force search over all permutations and thresholds to maximize accuracy against 797 survey self-reports; they determine the race labels for all 5,670 faculty and therefore all racial disparity analyses.
  • Gender meta-labeler thresholds = NonQuamGender threshold 0.85 for East Asian names, 0.75 otherwise
    Thresholds selected to maximize alignment with 811 survey respondents; they determine gender labels used in the centrality disparity comparisons.
  • Placement regression coefficients (M1, M2, M3) = Not reported numerically; standardized coefficients shown in Figure 5
    Linear regression models fitted to 2,041 early-career scholars to predict current institution rank from Ph.D. rank, closeness centrality, and degree. These coefficients are used to estimate the placement improvement from the intervention.
  • Institution rank bins for intervention targets = Bins: 21-37, 38-60, 61-83, 84-114, 115-157
    Chosen by hand to group institutions; targets are defined as minoritized race faculty in each bin. The bin boundaries are arbitrary and affect the composition of target groups.
  • Ph.D. network time window t1+5 = 5 years after first publication
    Chosen to approximate the network at Ph.D. completion; if this window includes post-placement collaborations, the placement model's predictive power may be overstated.
  • Sponsor group prestige threshold = top-10 or top-20 ranked institutions by placement power
    Sponsors are randomly selected from these sets; the paper reports little difference between top-10 and top-20, but the choice is a free design parameter.
assumptions (7)
  • domain assumption DBLP accurately records the coauthorship relations of CS faculty
    The entire network is built from DBLP; publications in venues not indexed by DBLP (e.g., Science, PNAS) are excluded, which the authors acknowledge may omit some interdisciplinary faculty.
  • domain assumption Perception-based and name-based demographic labels, combined by the meta-labeler, are valid proxies for socially constructed race and gender
    The labels are calibrated on a 15% survey response; misclassification for Black and MENA scholars is acknowledged, and labels reflect perception rather than self-identification.
  • domain assumption Survey respondents are representative of the full census
    The meta-labeler thresholds and accuracy estimates rely on 820 self-selected respondents; no non-response bias analysis is provided.
  • domain assumption The current institution of early-career faculty is their first post-Ph.D. placement
    Stated in Section 6.2 to justify using current institutional rank as the placement outcome in M1-M3; the authors note most faculty stay at their first job.
  • domain assumption The Ph.D. coauthorship network (publications up to t1+5) represents the network at job market entry
    Used to compute Ph.D. centrality for the placement models and the Ph.D. intervention; the window may include post-placement papers.
  • domain assumption Closeness centrality is a meaningful proxy for access to information and career advantage
    The paper interprets higher closeness as better access to and ability to spread information, a standard but unverified interpretation.
  • domain assumption Adding a single simulated edge is a valid model of a real facilitated collaboration
    The intervention assumes a new coauthorship edge appears as a result of pairing target and sponsor, and that the network remains otherwise unchanged.

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Cite this review

Pith. "Pith review of Edge interventions can mitigate demographic and prestige disparities in the Computer Science coauthorship network." pith.science (2026). https://pith.science/paper/IV2RH2MJ

@misc{pith2026250604435,
  author       = {Pith},
  title        = {Pith review of: Edge interventions can mitigate demographic and prestige disparities in the Computer Science coauthorship network},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IV2RH2MJ}},
  note         = {Machine review of arXiv:2506.04435}
}
read the original abstract

Social factors such as demographic traits and institutional prestige structure the creation and dissemination of ideas in academic publishing. One place these effects can be observed is in how central or peripheral a researcher is in the coauthorship network. Here we investigate inequities in network centrality in a hand-collected data set of 5,670 U.S.-based faculty employed in Ph.D.-granting Computer Science departments and their DBLP coauthorship connections. We introduce algorithms for combining name- and perception-based demographic labels by maximizing alignment with self-reported demographics from a survey of faculty from our census. We find that women and individuals with minoritized race identities are less central in the computer science coauthorship network, implying worse access to and ability to spread information. Centrality is also highly correlated with prestige, such that faculty in top-ranked departments are at the core and those in low-ranked departments are in the peripheries of the computer science coauthorship network. We show that these disparities can be mitigated using simulated edge interventions, interpreted as facilitated collaborations. Our intervention increases the centrality of target individuals, chosen independently of the network structure, by linking them with researchers from highly ranked institutions. When applied to scholars during their Ph.D., the intervention also improves the predicted rank of their placement institution in the academic job market. This work was guided by an ameliorative approach: uncovering social inequities in order to address them. By targeting scholars for intervention based on institutional prestige, we are able to improve their centrality in the coauthorship network that plays a key role in job placement and longer-term academic success.

Figures

Figures reproduced from arXiv: 2506.04435 by the authors.

Figure 1
Figure 1. (Left) Confusion matrices showing agreement between our meta-labeling algorithms and survey self-reports. (Right) [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. (Left) University prestige (rank, using the [ [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. We found similar patterns for betweenness centrality and [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (9 more)
Figure 3
Figure 3. Figure 3: Comparison of closeness centralities by demographic label and by institutional prestige, showing significant differences [PITH_FULL_IMAGE:figures/full_fig_p010_3.png]
Figure 4
Figure 4. Figure 4: (Left) Schematic of the proposed intervention to improve the centrality of target individuals. (Right) This simulated [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]
Figure 5
Figure 5. Figure 5: Model M1 line of best fit through a random subset of 150 points from fit data (Left) and standardized coefficients [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Simulated collaboration adds one edge between sponsor from top-ranked institution and racially minoritized target [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: University prestige (rank, using the [50] measure of placement power; smaller score is more prestigious) versus the mean closeness centrality of faculty at that institution. ns *** **** * **** **** [PITH_FULL_IMAGE:figures/full_fig_p022_7.png]
Figure 8
Figure 8. Figure 8: The p-values reported are from pooled t-tests comparing the betweenness centrality (left) and degree (right) of women [PITH_FULL_IMAGE:figures/full_fig_p022_8.png]
Figure 9
Figure 9. Figure 9: This simulated intervention results in increases in the closeness centrality of target individuals with minoritized race [PITH_FULL_IMAGE:figures/full_fig_p023_9.png]
Figure 10
Figure 10. Figure 10: USNWR: Simulated collaboration increases the centrality of target individuals at low ranked institutions and [PITH_FULL_IMAGE:figures/full_fig_p023_10.png]
Figure 11
Figure 11. Figure 11: CSRankings: Simulated collaboration increases the centrality of target individuals at low ranked institutions and [PITH_FULL_IMAGE:figures/full_fig_p024_11.png]

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Forecasting Faculty Placement from Patterns in Co-authorship Networks

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    Co-authorship network features improve out-of-sample prediction of whether a computer science PhD's first faculty position is at a top-10 department, beyond PhD rank and bibliometrics.

Reference graph

Works this paper leans on

66 extracted references · 66 canonical work pages · cited by 1 Pith paper

  1. [1]

    Shlomo Argamon, Moshe Koppel, Jonathan Fine, and Anat Rachel Shimoni. 2003. Gender, genre, and writing style in formal written texts.Text & talk23, 3 (2003), 321–346

    work page 2003

  2. [2]

    Sullivan, Sorelle Friedler, and Aaron Clauset

    Kate Barnes, Mia Ellis-Einhorn, Carolina Chavez-Ruelas, Nayera Hasan, Mo- hammad Fanous, Blair D. Sullivan, Sorelle Friedler, and Aaron Clauset. 2025. Edge interventions can mitigate demographic and prestige disparities in the Computer Science coauthorship network. https://github.com/k-barnes/cs-us- faculty-collaboration-network

    work page 2025

  3. [3]

    Ashkan Bashardoust, Sorelle Friedler, Carlos Scheidegger, Blair D Sullivan, and Suresh Venkatasubramanian. 2023. Reducing Access Disparities in Networks using Edge Augmentation. InProceedings of the 2023 ACM conference on fairness, accountability, and transparency. 1635–1651

    work page 2023

  4. [4]

    Bastedo and Nicholas A

    Michael N. Bastedo and Nicholas A. Bowman. 2010. U.S. News & World Report College Rankings: Modeling Institutional Effects on Organizational Reputation. American Journal of Education116 (2010), 163–183

    work page 2010

  5. [5]

    Ruben Becker, Gianlorenzo D’Angelo, and Sajjad Ghobadi. 2023. Improving fairness in information exposure by adding links. InProceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 14119–14126

    work page 2023

  6. [6]

    Nicolas Bérubé, Gita Ghiasi, Maxime Sainte-Marie, and Vincent Larivière. 2020. Wiki-Gendersort: Automatic gender detection using first names in Wikipedia. (2020)

    work page 2020

  7. [7]

    Aditya Bhaskara, Alex Crane, Shweta Jain, Md Mumtahin Habib Ullah Mazumder, Blair D Sullivan, and Prasanth Yalamanchili. 2024. Optimizing Information Access in Networks via Edge Augmentation.arXiv preprint arXiv:2407.02624(2024)

    work page Pith review arXiv 2024

  8. [8]

    Gunderson, Mary E

    Gecia Bravo-Hermsdorff, Valkyrie Felso, Emily Ray, Lee M. Gunderson, Mary E. Helander, Joana Maria, and Yael Niv. 2019. Gender and collaboration patterns in a temporal scientific authorship network.Applied Network Science4 (2019), 112

    work page 2019

Show all 66 references
  1. [9]

    Census Bureau

    U.S. Census Bureau. 2024. Updates to Race/Ethnicity Standards for Our Na- tion. https://www.census.gov/about/our-research/race-ethnicity/standards- updates.html

    2024

  2. [10]

    Ronald S Burt. 2004. Structural holes and good ideas.American journal of sociology110, 2 (2004), 349–399

    2004

  3. [11]

    Larremore

    Ian Van Buskirk, Aaron Clauset, and Daniel B. Larremore. 2023.An Open-Source Cultural Consensus Approach to Name-Based Gender Classification. Proceedings of the International AAAI Conference on Web and Social Media. doi:10.1609/ icwsm.v17i1.22195

    2023

  4. [12]

    Casad, J.E

    B.J. Casad, J.E. Franks, C.E. Garasky, M.M. Kittleman, Roesler A.C., Hall D.Y., and Z. W. Petzel. 2021. Gender inequality in academia: Problems and solutions for women faculty in STEM.J Neurosci Res.99 (2021), 13–23

    2021

  5. [13]

    Larremore

    Aaron Clauset, Samuel Arbesman, and Daniel B. Larremore. 2015. Systematic inequality and hierarchy in faculty hiring networks.Science advances1, 1 (2015)

    2015

  6. [14]

    Theodosius Dobzhansky. 1973. Nothing in Biology Makes Sense Except in the Light of Evolution.The American Biology Teacher(1973)

    1973

  7. [15]

    C. H. Dupree and C. M. Boykin. 2021. Racial Inequality in Academia: Systemic Origins, Modern Challenges, and Policy Recommendations.Policy Insights from the Behavioral and Brain Sciences8 (2021), 11–18

    2021

  8. [16]

    Dirk M Elston. 2021. Participation bias, self-selection bias, and response bias. Journal of the American Academy of Dermatology(2021)

    2021

  9. [17]

    Golnoosh Farnad, Behrouz Babaki, and Michel Gendreau. 2020. A unifying framework for fairness-aware influence maximization. InCompanion Proceedings of the Web Conference 2020. 714–722

    2020

  10. [18]

    Benjamin Fish, Ashkan Bashardoust, Danah Boyd, Sorelle Friedler, Carlos Schei- degger, and Suresh Venkatasubramanian. 2019. Gaps in information access in social networks. InThe World Wide Web Conference. 480–490

    2019

  11. [19]

    Freeman and Wei Huang

    Richard B. Freeman and Wei Huang. 2015. Collaborating with People Like Me: Ethnic Coauthorship within the United States.Journal of Labor Economics33 (2015), S289–S318

    2015

  12. [20]

    Vagrant Gautam, Arjun Subramonian, Anne Lauscher, and Os Keyes. 2024. Stop! in the name of flaws: Disentangling personal names and sociodemographic attributes in NLP.arXiv preprint arXiv:2405.17159(2024)

    2024 arXiv

  13. [21]

    Sally Haslanger. 2000. Gender and Race:(What) Are They?(What) Do We Want Them to Be? 1.Nous34, 1 (2000), 31–55

    2000

  14. [22]

    Hoppe, Aviva Litovitz, Kristine A

    Travis A. Hoppe, Aviva Litovitz, Kristine A. Willis, Rebecca A. Meseroll, Matthew J. Perkins, Alison F. Davis B. Ian Hutchins and, Michael S. Lauer, Han- nah A. Valantine, James M. Anderson, and George M. Santangelo. 2019. Topic choice contributes to the lower rate of NIH awar...

    2019

  15. [23]

    Gates, Roberta Sinatra, and Albert-László Barabási

    Junming Huang, Alexander J. Gates, Roberta Sinatra, and Albert-László Barabási

  16. [24]

    Fariba Karimi, Mathieu Génois, Claudia Wagner, Philipp Singer, and Markus Strohmaier. 2018. Homophily influences ranking of minorities in social networks. Scientific reports8, 11077 (2018), 1–12

    2018

  17. [25]

    Diego Kozlowski, Dakota S Murray, Alexis Bell, Will Hulsey, Vincent Larivière, Thema Monroe-White, and Cassidy R Sugimoto. 2022. Avoiding bias when inferring race using name-based approaches.Plos one17, 3 (2022)

    2022

  18. [26]

    Marek Kwiek and Wojciech Roszka. 2021. Gender-based homophily in research: A large-scale study of man-woman collaboration.Journal of Informetrics15, 3 (2021)

    2021

  19. [27]

    Nicholas LaBerge, Kenneth Hunter Wapman, Aaron Clauset, and Daniel B Lar- remore. 2024. Gendered hiring and attrition on the path to parity for academic faculty.eLife13 (2024)

    2024

  20. [28]

    Morgan, Sam Zhang, Daniel B

    Nicholas LaBerge, Kenneth Hunter Wapman, Allison C. Morgan, Sam Zhang, Daniel B. Larremore, and Aaron Clauset. 2022. Subfield Prestige and Gender Inequality among U.S. Computing Faculty.Commun. ACM65, 12 (2022), 46–55

    2022

  21. [29]

    2022.ethnicolr: Predict Race and Ethnicity From Name

    Suriyan Laohaprapanon, Gaurav Sood, and Bashar Naji. 2022.ethnicolr: Predict Race and Ethnicity From Name. https://github.com/appeler/ethnicolr

    2022

  22. [30]

    Vincent Lariviére, Benoit Macaluso, Éric Archambault, and Yves Gingras. 2010. Which scientific elites? On the concentration of research funds, publications and citations.Research Evaluation19, 1 (2010), 45–53

    2010

  23. [31]

    Larremore

    Eun Lee, Aaron Clauset, and Daniel B. Larremore. 2012. The dynamics of faculty hiring networks.EPJ Data Science10, 48 (2012)

    2012

  24. [32]

    Cranmer, and Aaron Clauset

    Weihua Li, Sam Zhang, Zhiming Zheng, Skyler J. Cranmer, and Aaron Clauset

  25. [33]

    Lockhart, Molly M

    Jeffrey W. Lockhart, Molly M. King, and Christin Munsch. 2023. Name-based demographic inference and the unequal distribution of misrecognition.Nature Human Behaviour7, 7 (2023), 1084–1095

    2023

  26. [34]

    Andrea Löther and Frederike Freund. 2024. Gender-Based Homophily in Inter- national Research Collaborations.Social Sciences13, 549 (2024). Edge interventions can mitigate demographic and prestige disparities in the Computer Science coauthorship network

    2024

  27. [35]

    Aubin, Michelle Penn-Marshall, Erika Henderson, Peggy Lindner, Cynthia Werner, Tracey Rizzuto, and Juan Madera

    Theodore Masters-Waage, Christiane Spitzmueller, Ebenezer Edema-Sillo, Ally St. Aubin, Michelle Penn-Marshall, Erika Henderson, Peggy Lindner, Cynthia Werner, Tracey Rizzuto, and Juan Madera. 2024. Underrepresented minority faculty in the USA face a double standard in promotio...

    2024

  28. [36]

    Morgan, Dimitrios J

    Allison C. Morgan, Dimitrios J. Economou, Samuel F. Way, and Aaron Clauset

  29. [37]

    Morgan, Nicholas LaBerge, Daniel B

    Allison C. Morgan, Nicholas LaBerge, Daniel B. Larremore, Mirta Galesic, Jen- nie E. Brand, and Aaron Clauset. 2022. Socioeconomic roots of academic faculty. Nature Human Behaviour6 (2022), 1625–1633

    2022

  30. [38]

    Namesor. [n. d.]. Namsor, name checker for gender, origin and ethnicity deter- mination. https://namsor.app. year accessed: 2024

    2024

  31. [39]

    M. E. J. Newman. 2004. Coauthorship networks and patterns of scientific col- laboration.Proceedings of the National Academy of Sciences USA101 (2004), 5200–5205

    2004

  32. [40]

    Emre Sarigöl, René Pfitzner, Ingo Scholtes, Antonios Garas, and Frank Schweitzer

  33. [41]

    Akrati Saxena, George Fletcher, and Mykola Pechenizkiy. 2024. Fairsna: Al- gorithmic fairness in social network analysis.Comput. Surveys56, 8 (2024), 1–45

    2024

  34. [42]

    Morgan Klaus Scheuerman and Jed R Brubaker. 2018. Gender is not a Boolean: towards designing algorithms to understand complex human identities. InIn Participation+ Algorithms Workshop at CSCW 2018

    2018

  35. [43]

    I. H. Settles, M. K. Jones, N. T. Buchanan, and K. Dotson. 2021. Epistemic exclusion: Scholar(ly) devaluation that marginalizes faculty of color.Journal of Diversity in Higher Education14, 4 (2021), 493–507

    2021

  36. [44]

    Katie Spoon, Nicholas LaBerge, Kenneth Hunter Wapman, Sam Zhang, Allison C Morgan, Mirta Galesic, Bailey K Fosdick, Daniel B Larremore, and Aaron Clauset

  37. [45]

    Katie Spoon, Joanna Mendy, Maria Martinez, Mirta Galesic, Daniel B Larremore, Aaron Clauset, and Lauren A Rivera. 2024. Gendered devaluation underlies faculty retention. (2024). Preprint, socarxiv/g6xwk_v1

    2024

  38. [46]

    Ana-Andreea Stoica and Augustin Chaintreau. 2019. Fairness in social influence maximization. InCompanion Proceedings of The 2019 World Wide Web Conference. 569–574

    2019

  39. [47]

    Torvik and Sneha Agarwal

    Vetle I. Torvik and Sneha Agarwal. 2016.Ethnea: an instance-based ethnicity classifier based on geo-coded author names in a large-scale bibliographic database. Proceedings of the International AAAI Conference on Web and Social Media

    2016

  40. [48]

    Lee Giles

    Pucktada Treeratpituk and C. Lee Giles. 2012. Name-ethnicity classification and ethnicity-sensitive name matching. InProceedings of the AAAI conference on artificial intelligence, Vol. 26. 1141–1147

    2012

  41. [49]

    Madalina Vlasceanu, Miroslav Dudik, and Ida Momennejad. 2022. Interdisci- plinarity, Gender Diversity, and Network Structure Predict the Centrality of AI Organizations. InProceedings of the 2022 ACM Conference on Fairness, Account- ability, and Transparency(Seoul, Republic of ...

    2022

  42. [50]

    Larremore

    Kenneth Hunter Wapman, Sam Zhang, Aaron Clauset, and Daniel B. Larremore

  43. [51]

    1994.Social Network Analysis: Methods and Applications

    Stanley Wasserman and Katherine Faust. 1994.Social Network Analysis: Methods and Applications. Cambridge University Press

    1994

  44. [52]

    Samuel F Way, Allison C Morgan, Daniel B Larremore, and Aaron Clauset. 2019. Productivity, prominence, and the effects of academic environment.Proceedings of the National Academy of Sciences116, 22 (2019), 10729–10733

    2019

  45. [53]

    Sasha Weitman. 1981. Some methodological issues in quantitative onomastics. 181–196 pages

    1981

  46. [54]

    Damani K White-Lewis. 2020. The facade of fit in faculty search processes.The Journal of Higher Education91, 6 (2020), 833–857

    2020

  47. [55]

    King, and Isabella Cingolani

    Kjersten Bunker Whittington, Molly M. King, and Isabella Cingolani. 2024. Struc- ture, status, and span: gender differences in co-authorship networks across 16 region-subject pairs (2009–2013).Scientometrics129 (2024), 147–179

    2024

  48. [56]

    Nature610, 7930 (2022), 120–127

    Quantifying hierarchy and dynamics in US faculty hiring and retention. Nature610, 7930 (2022), 120–127

    2022

  49. [57]

    2003.Making things happen: A theory of causal explanation

    James Woodward. 2003.Making things happen: A theory of causal explanation. Oxford university press

    2003

  50. [58]

    Xiao Han T Zeng, Jordi Duch, Marta Sales-Pardo, João AG Moreira, Filippo Radicchi, Haroldo V Ribeiro, Teresa K Woodruff, and Luís A Nunes Amaral. 2016. Differences in collaboration patterns across discipline, career stage, and gender. PLoS biology14, 11 (2016)

    2016

  51. [59]

    Sam Zhang, Kenneth Hunter Wapman, Daniel B Larremore, and Aaron Clauset

  52. [62]

    Dennis Robert Windham, Caroline J Wendt, Alex Crane, Madelyn J Warr, Freda Shi, Sorelle A Friedler, Blair D Sullivan, and Aaron Clauset. 2024. Fast algorithms to improve fair information access in networks.arXiv preprint arXiv:2409.03127 (2024)

    2024 arXiv

  53. [66]

    Update- dInstitution

    Labor advantages drive the greater productivity of faculty at elite universi- ties.Science advances8, 46 (2022). A Research Methods A.1 Coding Procedure The following coding procedure was used to collect faculty demo- graphic information. (1) Find the institution’s CS faculty ...

    2022

  54. [2014]

    Predicting scientific success based on coauthorship networks.EPJ Data Science9 (2014)

    2014

  55. [2018]

    Prestige drives epistemic inequality in the diffusion of scientific ideas.EPJ Data Science7, 40 (2018), 40

    2018

  56. [2020]

    Historical comparison of gender inequality in scientific careers across countries and disciplines.Proceedings of the National Academy of Science USA 117, 9 (2020), 4609–4616

    2020

  57. [2022]

    Untangling the network effects of productivity and prominence among scientists.Nature Communications13 (2022), 4907

    2022

  58. [2023]

    Gender and retention patterns among US faculty.Science advances9, 42 (2023)

    2023

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