About

Daniel McFarland is a faculty member at the Stanford Program in Science, Technology & Society within the School of Humanities and Sciences. He holds a PhD and MA in Sociology from the University of Chicago, as well as a BA in Philosophy and a BA in Sociology from the same institution. His research focuses on the intellectual, social, and institutional dynamics of educational systems such as schools, classrooms, universities, and disciplines. He has conducted studies on classroom organization and interaction, the formation of adolescent relationships, social structures, and identities, as well as interdisciplinary collaboration and scientific innovation. His broad research interests have led him to collaborate across disciplines with linguists, computer scientists, and sociologists. This interdisciplinary work has resulted in studies of big data, methodological advances in social networks, language modeling, and the study of innovation. McFarland's work emphasizes understanding the social and institutional factors that influence educational and scientific environments, contributing to the fields of science, technology, and society through his research on data dynamics, organizational innovation, and social interactions.

Research topics

• Computer science
• Sociology
• Data science
• Psychology
• Social psychology

Selected publications

• Engineering of Inquiry: The “Transformation” of Social Science through Generative AI

  2025-01-07 · 1 citations

  preprintOpen accessSenior author

  We increasingly read that generative AI will “transform” the social sciences, but little to no work has conceptualized the conditions necessary to fulfill such a promise. We review recent research on generative AI and evaluate its potential to reshape research practices. As the technology advances, generative AI could support various research tasks, including idea generation, data collection, and analysis. However, we discuss three challenges to an optimistic outlook that focuses solely on accelerating research through practical tools and reducing costs through inexpensive “synthetic” data. First, generative AI raises severe concerns about the validity of conclusions drawn from synthetic data about human populations. Second, possible efficiency gains in the research process may be partially offset by new problems introduced by the technology. Third, applications of generative AI have so far focused on enhancing existing methods, with limited efforts to harness the technology’s unique potential to simulate human behavior in social environments. Sociologists could use sociological theories and methods to develop “generative agents.” A new “trading zone” could emerge where social scientists, statisticians, and computer scientists develop new methodologies to facilitate innovative lines of inquiry and produce scientifically valid conclusions.

  PublisherOA PDFDOI

• Specialization or Generalization? Robust Identity for Hollywood Careers

  Academy of Management Proceedings · 2025-07-01

  articleSenior author

  The literature on competitive project-based labor markets in industries such as film, music, and the gig economy, offer competing views on how individuals should position themselves to have successful careers. The ‘categorical imperative’ literature argues individuals should specialize in external market genres to signal clarity, legitimacy and competence to consumer audiences and financiers. Conversely, the ‘role as resource’ literature argues individuals should generalize their association with internal production roles to enhance their adaptability and collaboration within temporary creative teams. How do these literatures interrelate and which positioning strategy is most effective? To date, no empirical study has compared and integrated these competing views concerning successful project-based careers. We address this by focusing on the careers of directors, producers, screenwriters, and actors in Hollywood (1980–2019), and we find evidence that these literatures should be integrated for a more complete understanding of how careers are forged in project-based labor markets. We find that successful careers require multivocal identities that appeal to both internal and external environments, and these environments vary their distinct demands by role. When individuals are in roles that broker production teams with external audiences - like producers - they extend their career by categorical imperative alone, specializing in their producer role and certain movie genres. When individuals occupy roles inwardly focused on the creative process - like screenwriters and directors - they extend their careers by expanding their role-experiences to include writing, directing, and acting, all while specializing in a movie genre. This finding suggests a new theory of labor market careers that recognizes the importance multivocal demands on roles have for successful careers.

  PublisherDOI

• How Values and Uncertainty Shape Scientific Advance in Peer Review

  American Sociological Review · 2025-10-01

  articleSenior authorCorresponding

  Tens of thousands of scientists contribute to peer review as journal editors and reviewers of the millions of manuscripts submitted every year. How do they decide what is quality work? What values do they apply in evaluating which science merits publication and which does not? How do they respond to dissensus and uncertainty? Who has the greatest influence over the final outcome? This study combines close reading with large language models to analyze 80,000 reviews of 28,000 accepted and rejected manuscripts in engineering and the life sciences. By following reviewers’ value judgments and editorial decisions, we come to a different view of how epistemic cultures are practiced in journal science. Instead of a consensual dialogue revealing salient norms, we find reviewers differently weigh (“commensurate”) their judgments to attribute value to works. Their pluralistic viewpoints elevate uncertainty about the work, and editors respond by aligning with the most negative of reviewers. Surprisingly, we observe engineers and life scientists find the same epistemic criteria are salient, valued, and influential, with novelty and accuracy being primary. These results underscore how contingency and uncertainty are structural features of STEM peer review and essential to its effectiveness and legitimacy.

  PublisherDOI

• Interdisciplinary Research, Tenure Review, and Guardians of the Disciplinary Order

  The Journal of Higher Education · 2024-01-18 · 15 citations

  articleOpen accessSenior author

  While interdisciplinarity has been promoted in universities for decades, research suggests that untenured faculty struggle to receive recognition for their interdisciplinary research.Informed by the microfoundations of institutional theory and discursive legitimation, we examine how members of academic departments participate in the legitimation and reproduction of tenure and promotion norms in relation to disciplinary and interdisciplinary research in a prestigious private university.Our analysis draws on 59 interviews with department chairs, directors of interdisciplinary centers, and disciplinary and interdisciplinary untenured faculty in the STEM fields, the social sciences, and the humanities.Our findings reveal three mechanisms and processes through which tenure and promotion norms become legitimated and reproduced in academic departments: 1) institutional micro-practices concerned with evaluation and gatekeeping, 2) discursive legitimation of the expulsion of interdisciplinarity at the pre-tenure stage, and 3) scholarly positioning through discursive boundary strategies directed at rationalizing the expulsion of interdisciplinarity or the expansion of existing tenure and promotion norms.Taken together, these findings advance our understanding of the tensions between the promotion of interdisciplinary research in higher education institutions, reproduction of the disciplinary order in academic departments, and interdisciplinary early-career scholars' career advancement.

  PublisherOA PDFDOI

• Can Large Language Models Provide Useful Feedback on Research Papers? A Large-Scale Empirical Analysis

  NEJM AI · 2024-07-17 · 156 citations

  article
  PublisherDOI

• Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews

  arXiv (Cornell University) · 2024-03-11 · 65 citations

  preprintOpen access

  We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took place after the release of ChatGPT: ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023. Our results suggest that between 6.5% and 16.9% of text submitted as peer reviews to these conferences could have been substantially modified by LLMs, i.e. beyond spell-checking or minor writing updates. The circumstances in which generated text occurs offer insight into user behavior: the estimated fraction of LLM-generated text is higher in reviews which report lower confidence, were submitted close to the deadline, and from reviewers who are less likely to respond to author rebuttals. We also observe corpus-level trends in generated text which may be too subtle to detect at the individual level, and discuss the implications of such trends on peer review. We call for future interdisciplinary work to examine how LLM use is changing our information and knowledge practices.

  PublisherOA PDFDOI

• Coming into relations: How communication reveals and persuades relational decisions

  Social Networks · 2024-06-17 · 2 citations

  article1st authorCorresponding
  PublisherDOI

• When ERGMs Lead to Biased Samples: Reply to Kretschmer et al.

  American Journal of Sociology · 2023-09-01 · 1 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Academic Migration: Interdisciplinary and Interdepartmental Hierarchy, Closure, or Similarity?

  Zenodo (CERN European Organization for Nuclear Research) · 2023-09-07

  paratextOpen accessSenior author

  The last two decades have seen a surge in research initiatives in many scientific fields surrounding interdisciplinarity and interdepartmentalization. This has spawned many research centers on US university campuses supported by billions of raised university or federal grant money to educate students as well as to facilitate interdisciplinary exchange and collaborations between faculty. This is often led by the belief that both intra- and interdisciplinary exchange in science pushes research fields forward and accelerates breakthrough discovery. Interdisciplinary scientific collaboration is argued to pull together diverse insights from multiple bodies of knowledge, is unrestricted by disciplinary boundaries or semantics, and can draw from a larger methodological tool set.

  PublisherDOI

• How random is the review outcome? A systematic study of the impact of external factors on <i>eLife</i> peer review

  bioRxiv (Cold Spring Harbor Laboratory) · 2023-01-04 · 1 citations

  reviewOpen access

  Abstract The advance of science rests on a robust peer review process. However whether or not a paper is accepted can depend on random external factors--e.g. the timing of the submission, the matching of editors and reviewers--that are beyond the quality of the work. This article systematically investigates the impact of these random factors independent of the paper’s quality on peer review outcomes in a major biomedical journal, eLife . We analyzed all of the submissions to eLife between 2016 to 2018, with 23,190 total submissions. We examined the effects of random factors at each decision point in the review process, from the gatekeeping senior editors who may desk-reject papers to review editors and reviewers who recommend the final outcome. Our results suggest that the peer-review process in eLife is robust overall and that random external factors have relatively little quantifiable bias.

  PublisherOA PDFDOI

Recent grants

• Collaborative: DHB: Social Network Dynamics of Youth

  NSF · $283k · 2007–2010

• Collaborative Research: Modeling the Invention, Dissemination, and Translation of Scientific Concepts

  NSF · $313k · 2018–2020

• SCISIPBIO: Can consultation create a fairer scientific peer-review process?

  NSF · $944k · 2021–2025

• CDI-Type II: What drives the dynamic creation of science?

  NSF · $1.2M · 2008–2013

• SBP: Glass Ceilings to Diversity

  NSF · $560k · 2018–2021

Frequent coauthors

• Hancheng Cao

  Stanford University

  26 shared

• Xiang Ren

  23 shared

• Mengjie Cheng

  Changzhou University

  21 shared

• Zhepeng Cen

  17 shared

• Dan Jurafsky

  13 shared

• Daniel Scott Smith

  Poughkeepsie Public Library District

  9 shared

• James Moody

  Duke University

  7 shared

• James Zou

  Stanford University

  7 shared

Education

• PhD, Sociology

  The University of Chicago

  1999