James M. Zumel Dumlao

James M. Zumel Dumlao

PhD Candidate, University of Michigan School of Information

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The effect of reviewer geographical diversity on evaluations is reduced by anonymizing submissions

Published in SocArXiv (R&R at Science), 2024

Preprint We study how geographical diversity among research evaluators affects the success of research producers. If evaluators favor work from their own countries (homophily), then producers from countries well-represented in the evaluator pool will benefit from homophily more often (differential access to homophily), resulting in a “geographical representation bias.” We test if this bias exists in science publishing using peer review data on 205K submissions to 60 journals published by the Institute of Physics Publishing. We find evidence of both homophily and differential access to homophily. Reviewers from the same country as the corresponding author are 4.78 percentage points more likely to review positively compared to other reviewers of the same manuscript. Authors from countries well-represented in the reviewer pool (e.g. USA, China, India) are 8-9 times more likely to be evaluated by same-country reviewers and benefit from homophily. Exploiting a policy shock that led to some papers being reviewed anonymously shows that anonymization causally reduces country homophily to a statistically non-significant level and, consequently, reduces representation bias. Geographical representation bias may be widespread, benefitting authors from wealthier countries that historically produced more research and have greater representation in the evaluator pool. Anonymization is an attractive tool for reducing this bias.

Recommended citation: Zumel Dumlao, James M., and Misha Teplitskiy. 2024. "Lack of Peer Reviewer Diversity Advantages Authors from Wealthier Countries." SocArXiv. May 6. doi:10.31235/osf.io/754e3. https://osf.io/preprints/socarxiv/754e3

Posts

2nd ICSSI

less than 1 minute read

Published:

UMSI representing at ICSSI 2023

portfolio

publications

Reward Schemes, Competition, and Output within Scientific Teams

Published in UMSI Field Preliminary Milestone, 2024

Scientists often perform their work organized in laboratories. As lab teams become increasingly large, research management grows in its capacity to make science more useful and efficient. Generally, management choices influence output by modifying workplace conditions, and thereby the skill, effort, and time workers devote to production. Managers may choose team members’ reward scheme, and one option is to introduce within-team competition for incentives. In science, inter-lab competition is well-documented, while intra-lab competition is understudied. This field preliminary paper reviews prior work from economics, sociology, and labor studies relevant to individualistic and competitive reward schemes and output, and considers how findings in non-science settings might apply to scientific production. A survey and interview study is proposed to address the lack of theoretical clarity on how research management choices shape intra-lab competition and output at the lab and individual levels.

Recommended citation: Zumel Dumlao, James M.. 2024. "Reward Schemes, Competition, and Output within Scientific Teams." Field Preliminary Paper. December 26. https://jamesmzd.github.io/files/JMZD_field_prelim_paper_revision.pdf

Learning by Evaluating: Evidence from Academic Peer Review

Published in R&R at Management Science, 2025

Working Paper. The evaluation of innovative projects is an essential task for scientific and business organizations alike. While prior work has focused on the quality of evaluations and how they affect innovators, little is known about how the evaluation process affects evaluators themselves. We propose that evaluations provide evaluators with valuable learning opportunities by exposing them to relevant, cutting-edge knowledge. We test the argument in the setting of academic peer review, using administrative data from the Institute of Physics Publishing comprising 104,306 reviewer-manuscript pairs across 55 physical sciences journals. We find that evaluating a manuscript more than doubles the likelihood of a reviewer using that knowledge (by citation) within three years compared to qualified evaluators who were invited but unavailable. Evaluators’ geographic and intellectual distance to the work moderates the learning effects of evaluation. While unavailable evaluators from the same country as a paper’s corresponding author have significantly higher baseline citation rates, evaluators from different countries who reviewed the paper achieved similarly high citation levels, indicating that structured evaluation of external ideas can compensate for geographic boundaries in knowledge transfer. Intellectual proximity between evaluators’ recent work and the work being evaluated is associated with stronger learning effects. These findings show that expert evaluations benefit not only organizations (by improving resource allocation) but also the evaluators themselves, which helps explain why they participate in the laborious and seemingly under-incentivized work.

Recommended citation: Ayoubi, Charles and Dumlao, James and Teplitskiy, Misha, Learning by Evaluating: Evidence from Academic Peer Review (July 04, 2025). ESSEC Business School Research Paper, Available at SSRN: https://ssrn.com/abstract=5339282 or http://dx.doi.org/10.2139/ssrn.5339282 https://ssrn.com/abstract=5339282

Geographical diversity of peer reviewers shapes author success

Published in Proceedings of the National Academy of Sciences, 2025

Scientific institutions like funding agencies and journals rely on peer reviewers to select among competing submissions. How does the geographical diversity of reviewers affect which authors are selected? If reviewers typically favor submissions from their own countries, but reviewers from only some countries are well represented in the reviewer pool, this can create a “geographical representation bias” favoring authors from those well-represented countries. Using administrative data on 204,718 submissions to 60 STEM journals from the Institute of Physics Publishing, we find support for representation bias. Reviewers from the same country as the corresponding author are 4.78 percentage points more likely to review positively compared to other reviewers of the same manuscript. Authors from the United States of America, China, and India are 8 to 9 times more likely to be evaluated by same-country reviewers compared to less-represented countries with similar incomes. Furthermore, an instrumental variables analysis of an anonymization policy shock shows that anonymizing submissions does not significantly reduce same-country homophily. Thus, investments in reviewer diversification may be necessary to mitigate the structural advantage of authors from major science-producing countries and avoid blind spots in collective knowledge.

Recommended citation: J.M. Zumel Dumlao, & M. Teplitskiy, Geographical diversity of peer reviewers shapes author success, Proc. Natl. Acad. Sci. U.S.A. 122 (33) e2507394122, https://doi.org/10.1073/pnas.2507394122 (2025). https://doi.org/10.1073/pnas.2507394122

Generative AI Availability, Grades, and Student Satisfaction at a Large University

Published in arXiv, 2026

Working Paper. The spread of generative AI (GenAI) in higher education has raised concerns that students offload cognitive effort to AI, earning high grades without learning. If this ``GenAI substitution hypothesis’’ is true, grades should rise disproportionately in GenAI-susceptible courses–those relying more on assessments like take-home problem sets and essays rather than in-class exams. Substitution could also affect student satisfaction, measured here as self-reported understanding and interest in the subject, which prior research links to assessments. We test the substitution hypothesis using syllabus and administrative data from a large U.S. university (2016-2025; 138,386 students; 72,730 course offerings). We measure courses’ GenAI susceptibility using a human-validated LLM pipeline to extract assessment types from syllabi, and use a differences-in-differences design comparing outcomes across courses before and after ChatGPT’s release, while modeling COVID-19 pandemic effects as either persistent or transient. We find no significant differential effect of GenAI availability on grades overall or among previously lower-performing students. Effects on self-reported understanding are likewise insignificant; effects on interest are significant only assuming transient pandemic effects. Our findings temper concerns that GenAI inflates grades and reduces students’ satisfaction.

Recommended citation: J.M. Zumel Dumlao, Meng Wang, Zhonghan Xie, Junyao Hu, Ivan Bar, George Chaney III, Henry Gold, & Misha Teplitskiy, Generative AI Availability, Grades, and Student Satisfaction at a Large University, [https://doi.org/10.1073/pnas.2507394122](https://doi.org/10.48550/arXiv.2607.21534) (2026). https://arxiv.org/abs/2607.21534

talks

teaching

Teaching Assistant

ECON 311: Intermediate Microeconomics, University of San Francisco, Economics Department, 2020

Graded 5 problem sets for 34 students taught by Prof. Mario Muzzi.

Graduate Student Instructor

SI 301: Models of Social Information Processing, University of Michigan School of Information, 2024

Fall 2024, Introduction to network analysis and game theory for undergraduate students in the Information Analytics track

Graduate Student Instructor

SI 485: Information Analytics Capstone II, University of Michigan School of Information, 2025

Winter 2025, Advised 13 teams of Information Analytics undergraduate students on projects with organizational partners

Graduate Student Instructor

SI 313: Introduction to Quantitative Methods, University of Michigan School of Information, 2025

Fall 2025, Introduction to log analysis, surveys, and experiments for undergraduate students on the UX track