References

Abdurahman, Suhaib, Alireza Salkhordeh Ziabari, Alexander K. Moore, Daniel M. Bartels, and Morteza Dehghani. 2025. “A Primer for Evaluating Large Language Models in Social-Science Research.” Advances in Methods and Practices in Psychological Science 8 (2). https://doi.org/10.1177/25152459251325174.

Acemoglu, Daron, and Pascual Restrepo. 2022. “Tasks, Automation, and the Rise in U.S. Wage Inequality.” Econometrica 90 (5): 1973–2016. https://doi.org/10.3982/ecta19815.

Acharya, Deepak Bhaskar, Karthigeyan Kuppan, and B. Divya. 2025. “Agentic AI: Autonomous Intelligence for Complex Goals–A Comprehensive Survey.” IEEE Access 13: 18912–36. https://doi.org/10.1109/access.2025.3532853.

Aczel, Balazs et al. 2026. “Investigating the Analytical Robustness of the Social and Behavioural Sciences.” Nature 652: 135–42. https://doi.org/10.1038/s41586-025-09844-9.

Adcock, Robert, and David Collier. 2001. “Measurement Validity: A Shared Standard for Qualitative and Quantitative Research.” American Political Science Review 95 (3): 529–46. https://doi.org/10.1017/s0003055401003100.

Ahluwalia, Aman, and Suhrud Wani. 2024. “Leveraging Large Language Models for Web Scraping.” arXiv. https://doi.org/10.48550/ARXIV.2406.08246.

Ahmed, Nur, and Muntasir Wahed. 2020. “The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research.” https://arxiv.org/abs/2010.15581.

Aigner, Ernest, Jacob Greenspon, and Dani Rodrik. 2025. “The global distribution of authorship in economics journals.” World Development 189: 106926.

Albertson, Bethany, and Shana Kushner Gadarian. 2015. Anxious Politics: Democratic Citizenship in a Threatening World. Cambridge: Cambridge University Press.

Alegria, Sharla. 2023. “Race and Intersecting Inequalities in Machine Learning.” In The Oxford Handbook of the Sociology of Machine Learning, 311–26. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780197653609.013.23.

Alexander, Rohan. 2023. Telling Stories with Data: With Applications in R and Python. Chapman & Hall/CRC. https://tellingstorieswithdata.com/.

Alizadeh, Meysam, Maël Kubli, Zeynab Samei, Shirin Dehghani, Mohammadmasiha Zahedivafa, Juan D. Bermeo, Maria Korobeynikova, and Fabrizio Gilardi. 2025. “Open-source LLMs for text annotation: a practical guide for model setting and fine-tuning.” Journal of Computational Social Science 8 (1). https://doi.org/10.1007/s42001-024-00345-9.

Almaatouq, Abdullah, Mohammed Alsobay, Ming Yin, and Duncan J Watts. 2021. “Task complexity moderates group synergy.” Proceedings of the National Academy of Sciences 118 (36): e2101062118. https://doi.org/10.1073/pnas.2101062118.

Almaatouq, Abdullah, Joshua Aaron Becker, Michael Bernstein, Robert Botto, Eric Bradlow, Ekaterina Damer, Angela Lee Duckworth, et al. 2021. “Scaling up experimental social, behavioral, and economic science,” December. https://doi.org/10.31219/osf.io/wksv8\_v1.

Alvero, A. J., Dustin S. Stoltz, Oscar Stuhler, and Marshall A. Taylor. 2026. “Generative AI in Sociological Research: State of the Discipline.” Sociological Science 13 (January): 45–62. https://doi.org/10.15195/v13.a3.

Amano, Tatsuya, Valeria Ramı́rez-Castañeda, Violeta Berdejo-Espinola, Israel Borokini, Shawan Chowdhury, Marina Golivets, Juan David González-Trujillo, et al. 2023. “The manifold costs of being a non-native English speaker in science.” Edited by Ulrich Dirnagl. PLOS Biology 21 (7): e3002184. https://doi.org/10.1371/journal.pbio.3002184.

American Economic Association. 2026. “AER Editorial Policy.” American Economic Association. https://www.aeaweb.org/journals/aer/editorial-policy.

American Political Science Association. 2026. “Statement on the proposed elimination of the social, behavioral, and economic sciences directorate at the National Science Foundation.” https://apsanet.org/wp-content/uploads/2026/04/FINAL-Dissolution-of-SBE.pdf.

American Political Science Review. 2026. “Preparing Your Materials.” Cambridge University Press. https://www.cambridge.org/core/journals/american-political-science-review/information/author-instructions/preparing-your-materials.

Amershi, Saleema, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, et al. 2019. “Guidelines for Human-AI Interaction.” In CHI Conference on Human Factors in Computing Systems Proceedings (CHI 2019). https://doi.org/10.1145/3290605.3300233.

Andrews, Isaiah, and Maximilian Kasy. 2019. “Identification of and Correction for Publication Bias.” American Economic Review 109 (8): 2766–94. https://doi.org/10.1257/aer.20180310.

Anthropic. 2026. “System Card: Claude Mythos Preview.” https://cdn.sanity.io/files/4zrzovbb/website/7624816413e9b4d2e3ba620c5a5e091b98b190a5.pdf.

Arel-Bundock, Vincent, Ryan C. Briggs, Hristos Doucouliagos, Marco Mendoza Aviña, and T. D. Stanley. 2026. “Quantitative Political Science Research is Greatly Underpowered.” Journal of Politics 88 (1): 36–46. https://doi.org/10.1086/734279.

Argyle, Lisa P., Ethan C. Busby, Nancy Fulda, Joshua R. Gubler, Christopher Rytting, and David Wingate. 2023. “Out of One, Many: Using Language Models to Simulate Human Samples.” Political Analysis 31 (3): 337–51. https://doi.org/10.1017/pan.2023.2.

Argyle, Lisa P., Ethan C. Busby, Joshua R. Gubler, Bryce Hepner, Alex Lyman, and David Wingate. 2025. “Arti-‘fickle’intelligence: using LLMs as a tool for inference in the political and social sciences.” Nature Computational Science 5: 737–44. https://doi.org/10.1038/s43588-025-00843-4.

Argyle, Lisa P., Ethan C. Busby, Joshua R. Gubler, Alex Lyman, Justin Olcott, Jackson Pond, and David Wingate. 2025. “Testing theories of political persuasion using AI.” Proceedings of the National Academy of Sciences 122 (18). https://doi.org/10.1073/pnas.2412815122.

Arroyo-Machado, Wenceslao, Jinghuan Ma, Ting Chen, Timothy P. Johnson, S. Islam, L. Michalegko, et al. 2025. “Generative AI and Academic Scientists in US Universities: Perception, Experience, and Adoption Intentions.” PLOS ONE 20 (8): e0330416. https://doi.org/10.1371/journal.pone.0330416.

Ashwin, Julian, Aditya Chhabra, and Vijayendra Rao. 2025. “Using Large Language Models for Qualitative Analysis Can Introduce Serious Bias.” Sociological Methods & Research. https://doi.org/10.1177/00491241251338246.

Assad, Stephanie, Robert Clark, Daniel Ershov, and Lei Xu. 2024. “Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market.” Journal of Political Economy 132 (3): 723–71. https://doi.org/10.1086/726906.

Attewell, Paul. 2001. “Comment: The First and Second Digital Divides.” Sociology of Education 74 (3): 252–59. https://doi.org/10.2307/2673277.

Autor, D. H., F. Levy, and R. J. Murnane. 2003. “The Skill Content of Recent Technological Change: An Empirical Exploration.” The Quarterly Journal of Economics 118 (4): 1279–1333. https://doi.org/10.1162/003355303322552801.

Autor, David H., Lawrence F. Katz, and Melissa S. Kearney. 2008. “Trends in U.S. Wage Inequality: Revising the Revisionists.” Review of Economics and Statistics 90 (2): 300–323. https://doi.org/10.1162/rest.90.2.300.

Bail, Christopher A. 2024. “Can Generative AI Improve Social Science?” Proceedings of the National Academy of Sciences 121 (21): e2314021121. https://doi.org/10.1073/pnas.2314021121.

Barari, Soubhik, and Tyler Simko. 2023. “LocalView, a database of public meetings for the study of local politics and policy-making in the United States.” Scientific Data 10 (1). https://doi.org/10.1038/s41597-023-02044-y.

Barrie, Christopher, Elli Palaiologou, and Petter Törnberg. 2025. “Prompt Stability Scoring for Text Annotation with Large Language Models.” arXiv preprint. https://doi.org/10.48550/arXiv.2407.02039.

Barrie, Christopher, Alexis Palmer, and Arthur Spirling. 2025. “Replication for Language Models: Problems, Principles, and Best Practices for Political Science.” https://arthurspirling.org/documents/BarriePalmerSpirling\%5FTrustMeBro.pdf.

Bastani, Hamsa, Osbert Bastani, Alp Sungu, Haosen Ge, Özge Kabakcı, and Rei Mariman. 2025. “Generative AI without Guardrails Can Harm Learning: Evidence from High School Mathematics.” Proceedings of the National Academy of Sciences 122 (26): e2422633122. https://doi.org/10.1073/pnas.2422633122.

Baumann, Joachim, Paul Röttger, Aleksandra Urman, Albert Wendsjö, Flor Miriam Plaza-del-Arco, Johannes B. Gruber, and Dirk Hovy. 2025. “Large Language Model Hacking: Quantifying the Hidden Risks of Using Llms for Text Annotation.” arXiv preprint. https://doi.org/10.48550/arXiv.2509.08825.

Beatty, Sally. 2024. “Khan Academy and Microsoft Partner to Expand Access to AI Tools That Personalize Teaching and Help Make Learning Fun.” Microsoft Source, May 21, 2024. https://web.archive.org/web/20250325224614/https://news.microsoft.com/source/features/ai/khan-academy-and-microsoft-partner-to-expand-access-to-ai-tools/.

Bendor, Jonathan. 1995. “A Model of Muddling Through.” American Political Science Review 89 (4): 819–40. https://doi.org/10.2307/2082511.

———. 2010. Bounded rationality and politics. Berkeley: University of California Press.

———. 2015. “Incrementalism: Dead yet Flourishing.” Public Administration Review 75 (2): 194–205. https://doi.org/10.1111/puar.12333.

Benjamini, Yoav, and Yosef Hochberg. 1995. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society: Series B (Methodological) 57 (1): 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.

Bennett, Sue, Karl Maton, and Lisa Kervin. 2008. “The ’digital natives’ debate: A critical review of the evidence.” British Journal of Educational Technology 39 (5): 775–86. https://doi.org/10.1111/j.1467-8535.2007.00793.x.

Besiroglu, Tamay, Sage Andrus Bergerson, Amelia Michael, Lennart Heim, Xueyun Luo, and Neil Thompson. 2024. “The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny?” https://arxiv.org/abs/2401.02452.

Bianchini, Stefano, Moritz Müller, and Pierre Pelletier. 2025. “Drivers and Barriers of AI Adoption and Use in Scientific Research.” Technological Forecasting and Social Change 220: 124303. https://doi.org/10.1016/j.techfore.2025.124303.

Bisbee, James, Joshua D. Clinton, Cassy Dorff, Brenton Kenkel, and Jennifer M. Larson. 2024. “Synthetic Replacements for Human Survey Data? The Perils of Large Language Models.” Political Analysis 32 (4): 401–16. https://doi.org/10.1017/pan.2024.5.

Bjork, Elizabeth Ligon, and Robert A. Bjork. 2011. “Making Things Hard on Yourself, but in a Good Way: Creating Desirable Difficulties to Enhance Learning.” In Psychology and the Real World: Essays Illustrating Fundamental Contributions to Society, edited by Morton Ann Gernsbacher, Richard W. Pew, Leaetta M. Hough, and James R. Pomerantz, 55–64. Worth Publishers.

Bjork, Robert A. 1975. “Retrieval as a Memory Modifier.” In Information Processing and Cognition: The Loyola Symposium, edited by Robert L. Solso, 123–44. Hillsdale, NJ: Erlbaum.

Blair, J. C., R. S. Ryan, and L. A. Schutzenhofer. 2012. “Elements of Engineering Excellence.” NASA/CR—2012-217471. NASA. https://ntrs.nasa.gov/api/citations/20130000445/downloads/20130000445.pdf.

Block, Ray, Charles Crabtree, John B. Holbein, and J. Quin Monson. 2021. “Are Americans less likely to reply to emails from Black people relative to White people?” Proceedings of the National Academy of Sciences 118 (52). https://doi.org/10.1073/pnas.2110347118.

Bol, Thijs, Mathijs de Vaan, and Arnout van de Rijt. 2018. “The Matthew effect in science funding.” Proceedings of the National Academy of Sciences 115 (19): 4887–90. https://doi.org/10.1073/pnas.1719557115.

Boykis, Vicki. 2026a. “NASA Elements of Engineering Excellence.” April 5, 2026. https://web.archive.org/web/20260430181213/https://vickiboykis.com/2026/04/05/nasa-elements-of-engineering-excellence/.

———. 2026b. “Build Yourself Flowers.” April 20, 2026. https://web.archive.org/web/20260426035611/https://vickiboykis.com/2026/04/20/build-yourself-flowers/.

Braverman, Harry. 1974. Labor and monopoly capital: The degradation of work in the twentieth century. New York: Monthly Review Press.

Breznau, Nate, Eike Mark Rinke, Alexander Wuttke, Hung H. V. Nguyen, Muna Adem, Jule Adriaans, Amalia Alvarez-Benjumea, et al. 2022. “Observing Many Researchers Using the Same Data and Hypothesis Reveals a Hidden Universe of Uncertainty.” Proceedings of the National Academy of Sciences 119 (44): e2203150119. https://doi.org/10.1073/pnas.2203150119.

Briggs, Ryan C., Jonathan Mellon, and Vincent Arel-Bundock. 2026. “It Must Be Very Hard to Publish Null Results.” https://doi.org/10.31235/osf.io/zr5vf_v1.

Brodeur, Abel, and Bruno Barbarioli. 2025. “The Replication Engine: How to Build Automated Replication Infrastructure for Better, Faster Science.” Institute for Progress. https://ifp.org/the-replication-engine/.

Brodeur, Abel, Nikolai Cook, and Anthony Heyes. 2020. “Methods Matter: P-Hacking and Publication Bias in Causal Analysis in Economics.” American Economic Review 110 (11): 3634–60. https://doi.org/10.1257/aer.20190687.

Brodeur, Abel, Nikolai Cook, and Carina Neisser. 2024. “P-Hacking, Data Type and Data-Sharing Policy.” The Economic Journal 134 (659): 985–1018. https://doi.org/10.1093/ej/uead104.

Brodeur, Abel, Mathias Lé, Marc Sangnier, and Yanos Zylberberg. 2016. “Star Wars: The Empirics Strike Back.” American Economic Journal: Applied Economics 8 (1): 1–32. https://doi.org/10.1257/app.20150044.

Brodeur, Abel, David Valenta, Alexandru Marcoci, Juan P Aparicio, Derek Mikola, Bruno Barbarioli, Rohan Alexander, et al. 2025. “Comparing Human-Only, AI-Assisted, and AI-Led Teams on Assessing Research Reproducibility in Quantitative Social Science.” I4R Discussion Paper Series. https://www.iza.org/publications/dp/17645/comparing-human-only-ai-assisted-and-ai-led-teams-on-assessing-research-reproducibility-in-quantitative-social-science.

Brooks, Jr., Frederick P. 1995. The Mythical Man-Month: Essays on Software Engineering Anniversary Edition. Boston, MA: Addison Wesley Longman, Inc.

Brown, Megan A., Shubham Atreja, Libby Hemphill, and Patrick Y. Wu. 2025. “Evaluating how LLM annotations represent diverse views on contentious topics.” https://arxiv.org/abs/2503.23243.

Bryan, Jenny. 2018. “Code Smells and Feels.” Presented at the userR!2018, Brisbane, July. https://github.com/jennybc/code-smells-and-feels.

Brynjolfsson, Erik, Bharat Chandar, and Ruyu Chen. 2025. “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence.” Stanford Digital Economy Lab. https://digitaleconomy.stanford.edu/publications/canaries-in-the-coal-mine/.

Butler, Daniel M., and Charles Crabtree. 2017. “Moving Beyond Measurement: Adapting Audit Studies to Test Bias-Reducing Interventions.” Journal of Experimental Political Science 4 (1): 57–67. https://doi.org/10.1017/xps.2017.11.

Calvano, Emilio, Giacomo Calzolari, Vincenzo Denicolò, and Sergio Pastorello. 2020. “Artificial Intelligence, Algorithmic Pricing, and Collusion.” American Economic Review 110 (10): 3267–97. https://doi.org/10.1257/aer.20190623.

Camerer, Colin F., Anna Dreber, Eskil Forsell, Teck-Hua Ho, Jürgen Huber, Magnus Johannesson, Michael Kirchler, et al. 2016. “Evaluating Replicability of Laboratory Experiments in Economics.” Science 351 (6280): 1433–36. https://doi.org/10.1126/science.aaf0918.

Carammia, Marcello, Stefano Maria Iacus, and Giuseppe Porro. 2024. “Rethinking Scale: The Efficacy of Fine-Tuned Open-Source LLMs in Large-Scale Reproducible Social Science Research.” https://arxiv.org/abs/2411.00890.

Carlini, Nicholas, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, and Chiyuan Zhang. 2023. “Quantifying Memorization across Neural Language Models.” Conference paper, The Eleventh International Conference on Learning Representations (ICLR 2023).

Carlson, Natalie A., and Vanessa Burbano. 2026. “The Use of Llms to Annotate Data in Management Research: Foundational Guidelines and Warnings.” Strategic Management Journal. https://doi.org/10.1002/smj.70023.

Chen, Lingjiao, Matei Zaharia, and James Zou. 2024. “How Is ChatGPT’s Behavior Changing over Time?” Harvard Data Science Review. https://doi.org/10.1162/99608f92.5317da47.

Cheng, Joe, and Sara Altman. 2025. “Databot Is Not a Flotation Device.” Posit. August 25, 2025. https://web.archive.org/web/20250910084327/https://posit.co/blog/databot-is-not-a-flotation-device/.

Chiang, Ted. 2024. “Why A.I. Isn’t Going to Make Art.” The New Yorker, August 31, 2024. https://www.newyorker.com/culture/the-weekend-essay/why-ai-isnt-going-to-make-art.

Chiang, Wei-Lin, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Hao Zhang, et al. 2024. “Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference.” https://arxiv.org/abs/2403.04132.

Chugunova, Marina, Dietmar Harhoff, Katharina Hölzle, Verena Kaschub, Sonal Malagimani, Ulrike Morgalla, and Robert Rose. 2026. “Who Uses AI in Research, and for What? Large-scale Survey Evidence from Germany.” Research Policy 55 (2): 105381. https://doi.org/10.1016/j.respol.2025.105381.

Clifford, Scott, and Spencer Piston. 2017. “Explaining Public Support for Counterproductive Homelessness Policy: The Role of Disgust.” Political Behavior 39 (2): 503–25. https://doi.org/10.1007/s11109-016-9366-4.

Cochrane, Christopher, Michael Cowan, Christopher Greenaway, and Rohan Alexander. 2026. “The Qualitative Turn in Quantitative Methods.”

Cockburn, Alistair. 2005. “Hexagonal Architecture.” 2005. https://alistair.cockburn.us/hexagonal-architecture/.

Coppock, Alexander, Seth J Hill, and Lynn Vavreck. 2020. “The small effects of political advertising are small regardless of context, message, sender, or receiver: Evidence from 59 real-time randomized experiments.” Science Advances 6 (36): eabc4046.

Correll, Shelley J. 2001. “Gender and the Career Choice Process: The Role of Biased Self-Assessments.” American Journal of Sociology 106 (6): 1691–1730. https://doi.org/10.1086/321299.

———. 2004. “Constraints into Preferences: Gender, Status, and Emerging Career Aspirations.” American Sociological Review 69 (1): 93–113. https://doi.org/10.1177/000312240406900106.

Correll, Shelley J., and Cecilia L. Ridgeway. 2006. “Expectation States Theory.” In Handbook of Social Psychology, edited by John Delamater, 29–51. Springer US. https://doi.org/10.1007/0-387-36921-x\_2.

Corrigan, Jack, Ngor Luong, and Christian Schoeberl. 2024. “Acquiring AI Companies: Tracking U.S. AI Mergers and Acquisitions.” https://cset.georgetown.edu/publication/acquiring-ai-companies-tracking-u-s-ai-mergers-and-acquisitions/.

Costello, Thomas H., Gordon Pennycook, and David G. Rand. 2024. “Durably reducing conspiracy beliefs through dialogues with AI.” Science 385 (6714). https://doi.org/10.1126/science.adq1814.

Cox, Emily, Fiona Shirani, and Paul Rouse. 2024. “Voices from the Algorithm: Large Language Models in Social Research.” Energy Research & Social Science 113: 103559. https://doi.org/10.1016/j.erss.2024.103559.

Crabtree, Charles, John B. Holbein, Mitchell Bosley, and Semra Sevi. 2026. “Can AI Help Reduce Prejudice? Evaluating the Effectiveness of AI-Powered Personalized Persuasion on Support for Transgender Rights.” PNAS Nexus.

Crabtree, Charles, John Holbein, Mitchell Bosley, and Semra Sevi. 2025. “Can AI Help Reduce Prejudice? Evaluating the Effectiveness of AI-Powered Personalized Persuasion on Support for Transgender Rights.” https://doi.org/10.2139/ssrn.5229084.

Criddle, Cristina. 2024. “Character.ai abandons making AI models after $2.7bn Google deal.” Financial Times. https://www.ft.com/content/f2a9b5d4-05fe-4134-b4fe-c24727b85bba.

Cruces, Guillermo, Diego Fernández Meijide, Sebastian Galiani, Ramiro H. Gálvez, and María Lombardi. 2026. “Does Generative AI Narrow Education-Based Productivity Gaps? Evidence from a Randomized Experiment.” 34851. National Bureau of Economic Research. https://doi.org/10.3386/w34851.

Dahl, Robert A. 1961. “The behavioral approach in political science: Epitaph for a monument to a successful protest.” American Political Science Review 55 (4): 763–72. https://doi.org/10.2307/1952525.

Dannefer, D. 2003. “Cumulative Advantage/Disadvantage and the Life Course: Cross-Fertilizing Age and Social Science Theory.” The Journals of Gerontology Series B: Psychological Sciences and Social Sciences 58 (6): S327–37. https://doi.org/10.1093/geronb/58.6.s327.

Davidovic, Jovana. 2023. “On the Purpose of Meaningful Human Control of AI.” Frontiers in Big Data 5. https://doi.org/10.3389/fdata.2022.1017677.

Davidson, Thomas. 2024. “Start Generating: Harnessing Generative Artificial Intelligence for Sociological Research.” Socius: Sociological Research for a Dynamic World 10 (January): 23780231241259651. https://doi.org/10.1177/23780231241259651.

Davidson, Thomas, and Daniel Karell. 2025. “Integrating Generative Artificial Intelligence into Social Science Research: Measurement, Prompting, and Simulation.” Sociological Methods & Research 54 (3): 775–93. https://journals.sagepub.com/eprint/W7ZYSTDICBC6JQGTTTGM/full.

Declaration on Research Assessment (DORA). 2012. “San Francisco Declaration on Research Assessment.” https://sfdora.org/read/.

Dell’Acqua, Fabrizio, Edward McFowland, Ethan Mollick, Hila Lifshitz, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon, and Karim R. Lakhani. 2026. “Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality.” Organization Science 37 (2): 403–23. https://doi.org/10.1287/orsc.2025.21838.

Deursen, Alexander J. A. M. van, and Ellen J. Helsper. 2015. “The Third-Level Digital Divide: Who Benefits Most from Being Online?” In Communication and Information Technologies Annual, 29–52. Emerald Group Publishing Limited. https://doi.org/10.1108/s2050-206020150000010002.

Deursen, Alexander van, and Jan van Dijk. 2019. “The first-level digital divide shifts from inequalities in physical access to inequalities in material access.” New Media & Society 21 (2): 354–75. https://doi.org/10.1177/1461444818797082.

Di Tillio, Alfredo, Marco Ottaviani, and Peter Norman Sørensen. 2021. “Strategic Sample Selection.” Econometrica 89 (2): 911–53. https://doi.org/10.3982/ECTA17288.

Dijk, Jan A. G. M. van. 2006. “Digital divide research, achievements and shortcomings.” Poetics 34 (4-5): 221–35. https://doi.org/10.1016/j.poetic.2006.05.004.

Dijk, Jan van. 2005. The Deepening Divide: Inequality in the Information Society. SAGE Publications, Inc. https://doi.org/10.4135/9781452229812.

DiMaggio, Hargittai, P., and S. Shafer. 2004. “Digital Inequality: From unequal access to differentiated use.” In Social Inequality, edited by K. M. Neckerman, 549–66. Russell Sage Foundation.

DiMaggio, Paul, and Eszter Hargittai. 2001. “From the ’Digital Divide’ to ’Digital Inequality’: Studying Internet Use as Penetration Increases.” Working Papers 47. Princeton University, School of Public; International Affairs, Center for Arts; Cultural Policy Studies. https://ideas.repec.org/p/pri/cpanda/15.html.

DiPrete, Thomas A., and Gregory M. Eirich. 2006. “Cumulative Advantage as a Mechanism for Inequality: A Review of Theoretical and Empirical Developments.” Annual Review of Sociology 32 (1): 271–97. https://doi.org/10.1146/annurev.soc.32.061604.123127.

Dittmar, Jeremiah E. 2011. “Information Technology and Economic Change: The Impact of The Printing Press .” The Quarterly Journal of Economics 126 (3): 1133–72. https://doi.org/10.1093/qje/qjr035.

Eaton, Asia A., Jessica F. Saunders, Ryan K. Jacobson, and Keon West. 2020. “How Gender and Race Stereotypes Impact the Advancement of Scholars in STEM: Professors’ Biased Evaluations of Physics and Biology Post-Doctoral Candidates.” Sex Roles 82 (3-4): 127–41. https://doi.org/10.1007/s11199-019-01052-w.

Egami, Naoki, Musashi Hinck, Brandon M. Stewart, and Hanying Wei. 2026. “Using Large Language Model Annotations for the Social Sciences: A General Framework of Using Predicted Variables in Downstream Analyses.” American Journal of Political Science.

Ellington, Aimee J. 2003. “A Meta-Analysis of the Effects of Calculators on Students’ Achievement and Attitude Levels in Precollege Mathematics Classes.” Journal for Research in Mathematics Education 34 (5): 433. https://doi.org/10.2307/30034795.

Evans, Eric. 2004. Domain-Driven Design: Tackling Complexity in the Heart of Software. Boston, MA: Addison-Wesley Professional.

Fan, Yizhou, Luzhen Tang, Huixiao Le, Kejie Shen, Shufang Tan, Yueying Zhao, Yuan Shen, Xinyu Li, and Dragan Gasevic. 2025. “Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performance.” British Journal of Educational Technology. https://doi.org/10.1111/bjet.13544.

Filimonovic, Dragan, Christian Rutzer, and Conny Wunsch. 2025. “Can GenAI Improve Academic Performance? Evidence from the Social and Behavioral Sciences.” https://arxiv.org/abs/2510.02408.

Fish, Sara, Yannai A. Gonczarowski, and Ran I. Shorrer. 2026. “Algorithmic Collusion by Large Language Models.” https://arxiv.org/abs/2404.00806.

Fishman, Nic, and Gabriel Sekeres. 2026. “Editorial Screening when Science is Cheap.” Working Paper. https://gabesekeres.com/papers/cheap\%5Fscience/gsekeres\%5Fcheap\%5Fscience.pdf.

Franco, Annie, Neil Malhotra, and Gabor Simonovits. 2014. “Publication Bias in the Social Sciences: Unlocking the File Drawer.” Science 345 (6203): 1502–5. https://doi.org/10.1126/science.1255484.

Frankel, Alexander, and Maximilian Kasy. 2022. “Which Findings Should Be Published?” American Economic Journal: Microeconomics 14 (1): 1–38. https://doi.org/10.1257/mic.20190133.

Gaddis, S. Michael, Evan N. Larsen, Charles Crabtree, and John B. Holbein. 2022. “Discrimination against Black and Hispanic Americans is highest in hiring and housing contexts: A meta-analysis of correspondence audits.” https://doi.org/10.2139/ssrn.3975770.

Gai, Phyliss Jia, Jiayi Hou, and Yanping Tu. 2025. “Competence Penalty Is a Barrier to the Adoption of New Technology.” https://doi.org/10.2139/ssrn.5255039.

Gao, Jian, and Dashun Wang. 2024. “Quantifying the use and potential benefits of artificial intelligence in scientific research.” Nature Human Behaviour 8 (12): 2281–92. https://doi.org/10.1038/s41562-024-02020-5.

Gebru, Timnit, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé Iii, and Kate Crawford. 2021. “Datasheets for datasets.” Communications of the ACM 64 (12): 86–92. https://doi.org/10.1145/3458723.

Gelman, Andrew, and Eric Loken. 2013. “The Garden of Forking Paths: Why Multiple Comparisons Can Be a Problem, Even When There Is No ‘Fishing Expedition’ or ‘P-Hacking’ and the Research Hypothesis Was Posited Ahead of Time.” https://sites.stat.columbia.edu/gelman/research/unpublished/p\%5Fhacking.pdf.

———. 2014. “The Statistical Crisis in Science.” American Scientist 102 (6): 460–65. https://doi.org/10.1511/2014.111.460.

Gerring, John. 1999. “What Makes a Concept Good? A Criterial Framework for Understanding Concept Formation in the Social Sciences.” Polity 31 (3): 357–93. https://doi.org/10.2307/3235246.

Gonzalez-Rostani, Valentina, and Shir Raviv. 2026. “Tracing AI Assistance and AI Agents in Survey Research.” https://doi.org/10.2139/ssrn.6576218.

Gordon, Sanford C., Cyrus Samii, and Zhihao Su. 2025. “Data-NoMAD: A Tool for Boosting Confidence in the Integrity of Social Science Survey Data.” arXiv preprint. https://doi.org/10.48550/arXiv.2501.14651.

Greengard, Samuel. 2025. “The AI Deskilling Paradox.” Communications of the ACM, November 7, 2025. https://web.archive.org/web/20251110043905/https://cacm.acm.org/news/the-ai-deskilling-paradox/.

Grimmer, Justin, Margaret E. Roberts, and Brandon M. Stewart. 2022. Text as data: A new framework for machine learning and the social sciences. Princeton: Princeton University Press.

Grimmer, Justin, and Brandon M. Stewart. 2013. “Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts.” Political Analysis 21 (3): 267–97. https://doi.org/10.1093/pan/mps028.

Grossman, Sanford J. 1981. “The Informational Role of Warranties and Private Disclosure About Product Quality.” Journal of Law and Economics 24 (3): 461–83. https://doi.org/10.1086/467025.

Grossmann, Igor, Matthew Feinberg, Dawn C Parker, Nicholas A Christakis, Philip E Tetlock, and William A Cunningham. 2023. “AI and the transformation of social science research.” Science 380 (6650): 1108–9.

Guo, Daya, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, et al. 2025. “DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning.” Nature 645 (8081): 633–38. https://doi.org/10.1038/s41586-025-09422-z.

Hall, Andy. 2026a. “The 100x Research Institution.” Substack Newsletter. Free Systems. https://freesystems.substack.com/p/the-100x-research-institution.

———. 2026b. “AI is already 10x-ing academic research. How do we get to 100x?” Substack Newsletter. The Roots of Progress. https://newsletter.rootsofprogress.org/p/ai-is-already-10x-ing-academic-research.

Halterman, Andrew, and Katherine A. Keith. 2026. “Codebook LLMs: Evaluating LLMs as Measurement Tools for Political Science Concepts.” Political Analysis 34 (2): 188–204. https://doi.org/10.1017/pan.2025.10017.

Handa, Kunal, Alex Tamkin, Miles McCain, Saffron Huang, Esin Durmus, Sarah Heck, Jared Mueller, et al. 2025. “Which Economic Tasks Are Performed with AI? Evidence from Millions of Claude Conversations.” arXiv. https://doi.org/10.48550/arXiv.2503.04761.

Hanson, Mark A, Pablo Gómez Barreiro, Paolo Crosetto, and Dan Brockington. 2024. “The strain on scientific publishing.” Quantitative Science Studies 5 (4): 823–43. https://doi.org/10.1162/qss_a_00327.

Hardinges, Jack, Elena Simperl, and Nigel Shadbolt. 2024. “We Must Fix the Lack of Transparency Around the Data Used to Train Foundation Models.” Harvard Data Science Review, no. Special Issue 5. https://doi.org/10.1162/99608f92.a50ec6e6.

Hargittai, Eszter. 2002. “Second-Level Digital Divide: Differences in People’s Online Skills.” First Monday 7 (4). https://doi.org/10.5210/fm.v7i4.942.

He, Yongyuan, and Yi Bu. 2026. “Academic journals’ AI policies fail to curb the surge in AI-assisted academic writing.” Proceedings of the National Academy of Sciences 123 (9). https://doi.org/10.1073/pnas.2526734123.

Heath, Anthony, Stephen Fisher, and Shawna Smith. 2005. “The Globalization Of Public Opinion Research.” Annual Review of Political Science 8 (1): 297–333. https://doi.org/10.1146/annurev.polisci.8.090203.103000.

Hembree, Ray, and Donald J. Dessart. 1986. “Effects of Hand-Held Calculators in Precollege Mathematics Education: A Meta-Analysis.” Journal for Research in Mathematics Education 17 (2): 83. https://doi.org/10.2307/749255.

Henry, Emeric. 2009. “Strategic Disclosure of Research Results: The Cost of Proving Your Honesty.” The Economic Journal 119 (539): 1036–64. https://doi.org/10.1111/j.1468-0297.2009.02265.x.

Henry, Emeric, and Marco Ottaviani. 2019. “Research and the Approval Process: The Organization of Persuasion.” American Economic Review 109 (3): 911–55. https://doi.org/10.1257/aer.20171919.

Henshall, Will. 2024. “There’s an AI Lobbying Frenzy in Washington. Big Tech Is Dominating.” Time. https://time.com/6972134/ai-lobbying-tech-policy-surge/.

Herresthal, Claudia. 2022. “Hidden Testing and Selective Disclosure of Evidence.” Journal of Economic Theory 200: 105312. https://doi.org/10.1016/j.jet.2021.105312.

Hicks, Cat. 2026a. “Learning Goal: A Claude Code Skill for Structured Goal Setting with Mental Contrasting.” https://github.com/DrCatHicks/learning-goal.

———. 2026b. “Learning Opportunities: A Claude Code Skill for Deliberate Skill Development.” https://github.com/DrCatHicks/learning-opportunities.

Hicks, Diana, Paul Wouters, Ludo Waltman, Sarah de Rijcke, and Ismael Rafols. 2015. “Bibliometrics: The Leiden Manifesto for research metrics.” Nature 520 (7548): 429–31. https://doi.org/10.1038/520429a.

Holst, Dirk, Keno Moenck, Julian Koch, Ole Schmedemann, and Thorsten Schüppstuhl. 2025. “Transparent Reporting of AI in Systematic Literature Reviews: Development of the PRISMA-trAIce Checklist.” JMIR AI. https://doi.org/10.2196/80247.

Horton, John J., Apostolos Filippas, and Benjamin S. Manning. 2026. “Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?” https://arxiv.org/abs/2301.07543.

Hosseini, Soodeh, and Hossein Seilani. 2025. “The role of agentic AI in shaping a smart future: A systematic review.” Array 26: 100399. https://doi.org/10.1016/j.array.2025.100399.

Houghton, James P., and Duncan J Watts. 2025. “The role of topic choice in cross-partisan conversations,” May. https://doi.org/10.31235/osf.io/nygt3\_v1.

Huh, Minyoung, Brian Cheung, Tongzhou Wang, and Phillip Isola. 2024. “Position: The Platonic Representation Hypothesis.” In Proceedings of the 41st International Conference on Machine Learning, edited by Ruslan Salakhutdinov, Zico Kolter, Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, and Felix Berkenkamp, 235:20617–42. Proceedings of Machine Learning Research. PMLR. https://proceedings.mlr.press/v235/huh24a.html.

Humlum, Anders, and Emilie Vestergaard. 2024. “The unequal adoption of ChatGPT exacerbates existing inequalities among workers.” Proceedings of the National Academy of Sciences 122 (1). https://doi.org/10.1073/pnas.2414972121.

Imas, Alex, and Soumitra Shukla. 2026. “How Will AI-driven Automation Actually Affect Jobs?: The Economics of AI Exposure and Job Displacement.” March 23, 2026. https://aleximas.substack.com/p/how-will-ai-driven-automation-actually.

Ioannidis, John P. A. 2005. “Why Most Published Research Findings Are False.” PLOS Medicine 2 (8): e124. https://doi.org/10.1371/journal.pmed.0020124.

Iyengar, Shanto, Gaurav Sood, and Yphtach Lelkes. 2012. “Affect, not ideology: A social identity perspective on polarization.” Public Opinion Quarterly 76 (3): 405–31. https://doi.org/10.1093/poq/nfs038.

Jin, Berber. 2025. “Anthropic is on track to turn a profit much faster than OpenAI.” The Wall Street Journal, November. https://www.wsj.com/tech/ai/openai-anthropic-profitability-e9f5bcd6.

Johnston, Theresa. 2015. “Jonathan Bendor: Why Criticism Is Good for Innovation.” https://www.gsb.stanford.edu/insights/jonathan-bendor-why-criticism-good-innovation.

Jošt, Gregor, Viktor Taneski, and Sašo Karakatič. 2024. “The impact of large language models on programming education and student learning outcomes.” Applied Sciences 14 (10): 4115. https://doi.org/10.3390/app14104115.

Joyce, Kelly, Laurel Smith-Doerr, Sharla Alegria, Susan Bell, Taylor Cruz, Steve G. Hoffman, Safiya Umoja Noble, and Benjamin Shestakofsky. 2021. “Toward a Sociology of Artificial Intelligence: A Call for Research on Inequalities and Structural Change.” Socius: Sociological Research for a Dynamic World 7 (January). https://doi.org/10.1177/2378023121999581.

Juavinett, Ashley. 2026. “How to Teach Programming in the Age of AI.” The Transmitter. https://doi.org/10.53053/IXOZ2506.

Juavinett, Ashley, and Cat Hicks. 2026. You Can Learn with AI. https://www.buzzsprout.com/2396236/episodes/18692591-you-can-learn-with-ai.

Kaiser, Tamás, Tamás Tóth, and Marton Demeter. 2023. “Publishing Trends in Political Science: How Publishing Houses, Geographical Positions, and International Collaboration Shapes Academic Knowledge Production.” Publishing Research Quarterly 39 (3): 201–18. https://doi.org/10.1007/s12109-023-09957-x.

Kamenica, Emir, and Matthew Gentzkow. 2011. “Bayesian Persuasion.” American Economic Review 101 (6): 2590–2615. https://doi.org/10.1257/aer.101.6.2590.

Kane, John V. 2025. “More than meets the ITT: A guide for anticipating and investigating nonsignificant results in survey experiments.” Journal of Experimental Political Science 12 (1): 110–25. https://doi.org/10.1017/xps.2024.1.

Karjus, Andres. 2025. “Machine-assisted quantitizing designs: augmenting humanities and social sciences with artificial intelligence.” Humanities and Social Sciences Communications 12 (1): 1–18. https://doi.org/10.1057/s41599-025-04503-w.

Kasy, Maximilian. 2021. “Of Forking Paths and Tied Hands: Selective Publication of Findings, and What Economists Should Do about It.” Journal of Economic Perspectives 35 (3): 175–92. https://doi.org/10.1257/jep.35.3.175.

Kasy, Maximilian, and Jann Spiess. 2025. “Optimal Pre-Analysis Plans: Statistical Decisions Subject to Implementability.” Working Paper. https://maxkasy.github.io/home/files/papers/optimal\%5Fpreanalysis\%5Fplans.pdf.

Kazimirov, Alexandros. 2025. “Mergers & Cooptive Acquisitions.” https://www.antitrustinstitute.org/wp-content/uploads/2025/04/AAI-Paper-Mergers-Cooptive-Acquisitions.pdf.

Kellogg, Katherine C., Melissa A. Valentine, and Angéle Christin. 2020. “Algorithms at Work: The New Contested Terrain of Control.” Academy of Management Annals 14 (1): 366–410. https://doi.org/10.5465/annals.2018.0174.

Khan, Farhan Kamrul, Hazem Ibrahim, Nouar Aldahoul, Talal Rahwan, and Yasir Zaki. 2025. “Who Gets Seen in the Age of AI? Adoption Patterns of Large Language Models in Scholarly Writing and Citation Outcomes.” https://arxiv.org/abs/2509.08306.

Kim, Geewook, Teakgyu Hong, Moonbin Yim, JeongYeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, and Seunghyun Park. 2022. “OCR-Free Document Understanding Transformer.” In Computer Vision – ECCV 2022, 498–517. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-19815-1\_29.

Kim, Jae Yeon, Milan de Vries, and Hahrie Han. 2025. “MapAgora, civic opportunity datasets for the study of American local politics and public policy.” Scientific Data 12 (1). https://doi.org/10.1038/s41597-025-05353-6.

King, Gary, Robert O. Keohane, and Sidney Verba. 2021. Designing Social Inquiry: Scientific Inference in Qualitative Research, new edition. Chicago: Princeton University Press.

King, Gary, Patrick Lam, and Margaret E. Roberts. 2017. “Computer-Assisted Keyword and Document Set Discovery from Unstructured Text.” American Journal of Political Science 61 (4): 971–88. https://doi.org/10.1111/ajps.12291.

King, Jennifer, and Tiffany Saade. 2026. “Data Privacy and Foundation Models: Can We Have Both?” Stanford Institute for Human-Centered AI.

Kirschner, Paul A., and Pedro De Bruyckere. 2017. “The myths of the digital native and the multitasker.” Teaching and Teacher Education 67 (October): 135–42. https://doi.org/10.1016/j.tate.2017.06.001.

Kleinberg, Jon, and Manish Raghavan. 2021. “Algorithmic monoculture and social welfare.” Proceedings of the National Academy of Sciences 118 (22): e2018340118. https://doi.org/10.1073/pnas.2018340118.

Korinek, Anton. 2023. “Generative AI for Economic Research: Use Cases and Implications for Economists.” Journal of Economic Literature. https://doi.org/10.1257/jel.20231736.

Kozlov, Max, Dan Garisto, and Edward Chen. 2026a. “Massive budget cuts for US science proposed again by Trump administration.” Nature 652 (8110): 547–48. https://doi.org/10.1038/d41586-026-01105-7.

———. 2026b. “Trump Administration Proposes Massive Budget Cuts to Science.” Scientific American, April. https://www.scientificamerican.com/article/trump-administration-proposes-massive-budget-cuts-to-science/.

Krippendorff, Klaus. 2019. Content Analysis: An Introduction to Its Methodology. Fourth. Thousand Oaks, California: SAGE Publications, Inc. https://doi.org/10.4135/9781071878781.

Kusumegi, Keigo, Xinyu Yang, Paul Ginsparg, Mathijs de Vaan, Toby Stuart, and Yian Yin. 2025. “Scientific production in the era of large language models.” Science 390 (6779): 1240–43. https://doi.org/10.1126/science.adw3000.

L., Jennifer. 2026. “NVIDIA Controls 92% of the GPU Market in 2025 and Reveals Next Gen AI Supercomputer.” https://carboncredits.com/nvidia-controls-92-of-the-gpu-market-in-2025-and-reveals-next-gen-ai-supercomputer/.

Lakatos, Imre. 1970. “Falsification and the methodology of scientific research programmes.” In Criticism and the Growth of Knowledge: Proceedings of the International Colloquium in the Philosophy of Science, London, 1965, edited by Imre Lakatos and Alan Musgrave, 91–196. New York: Cambridge University Press.

Lambert, Nathan, Jacob Morrison, Valentina Pyatkin, Shengyi Huang, Hamish Ivison, Faeze Brahman, Lester James V. Miranda, et al. 2025. “Tulu 3: Pushing Frontiers in Open Language Model Post-Training.” https://arxiv.org/abs/2411.15124.

Landau, Martin. 1969. “Redundancy, Rationality, and the Problem of Duplication and Overlap.” Public Administration Review 29 (4): 346–58. https://doi.org/10.2307/973247.

Latour, Bruno, and Steve Woolgar. 1986. “Laboratory Life: The Construction of Scientific Facts.”

Laurer, Moritz, Wouter van Atteveldt, Andreu Casas, and Kasper Welbers. 2025. “On Measurement Validity and Language Models: Increasing Validity and Decreasing Bias with Instructions.” Communication Methods and Measures 19 (1): 46–62. https://doi.org/10.1080/19312458.2024.2378690.

Le Mens, Gaël, and Aina Gallego. 2025. “Positioning Political Texts with Large Language Models by Asking and Averaging.” Political Analysis 33 (3): 274–82. https://doi.org/10.1017/pan.2024.29.

Leamer, Edward E. 1983. “Let’s Take the Con Out of Econometrics.” American Economic Review 73 (1): 31–43. https://www.jstor.org/stable/1803924.

Lee, Hao-Ping (Hank), Advait Sarkar, Lev Tankelevitch, Ian Drosos, Sean Rintel, Richard Banks, and Nicholas Wilson. 2025. “The Impact of Generative AI on Critical Thinking: Self-reported Reductions in Cognitive Effort and Confidence Effects from a Survey of Knowledge Workers.” In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3706598.3713778.

Lee, Kyuwon, Simone Paci, Jeongmin Park, Hye Young You, and Sylvan Zheng. 2025. “Applications of GPT in Political Science Research: Extracting Information from Unstructured Text.” PS: Political Science & Politics 58 (4): 630–40. https://doi.org/10.1017/S1049096525000046.

Lehmann, Matthias, Philipp B. Cornelius, and Fabian J. Sting. 2025. “AI Meets the Classroom: When Do Large Language Models Harm Learning?” March 8, 2025. https://doi.org/10.48550/arXiv.2409.09047.

Lei, Ya-Wen, and Rachel Kim. 2024. “Automation and augmentation: artificial intelligence, robots, and work.” Annual Review of Sociology 50 (1): 251–72. https://doi.org/10.1146/annurev-soc-090523-050708.

Licht, Hauke, Rupak Sarkar, Patrick Y. Wu, Pranav Goel, Niklas Stoehr, Elliott Ash, and Alexander Miserlis Hoyle. 2025. “Measuring scalar constructs in social science with LLMs.” In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, edited by Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, and Violet Peng, 32144–71. Suzhou, China: Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.emnlp-main.1635.

Lichtenberg, Nick. 2025. “Spending on AI data centers is so massive that it’s taken a bigger chunk of GDP growth than shopping—and it could crash the American economy.” Fortune. https://fortune.com/2025/08/06/data-center-artificial-intelligence-bubble-consumer-spending-economy/.

Lin, Zhicheng, and Aamir Sohail. 2026. “Recalibrating academic expertise in the age of generative AI.” Patterns 7 (1): 101473. https://doi.org/10.1016/j.patter.2025.101473.

Lindblom, Charles E. 1959. “The Science of "Muddling Through".” Public Administration Review 19 (2): 79–88. https://doi.org/10.2307/973677.

Lippert, Steffen, Anna Dreber, Magnus Johannesson, Warren Tierney, Wilson Cyrus-Lai, Eric Luis Uhlmann, Emotion Expression Collaboration, and Thomas Pfeiffer. 2024. “Can large language models help predict results from a complex behavioural science study?” Royal Society Open Science 11 (9): 240682. https://doi.org/10.1098/rsos.240682.

Liskov, Barbara. 1987. “Data Abstraction and Hierarchy.” In Addendum to the Proceedings on Object-Oriented Programming Systems, Languages and Applications (OOPSLA ’87), 17–34. New York, NY: Association for Computing Machinery. https://doi.org/10.1145/62138.62141.

Lodge, Milton, and Charles S. Taber. 2013. The Rationalizing Voter. Cambridge: Cambridge University Press.

Lu, Chris, Cong Lu, Robert Tjarko Lange, Yutaro Yamada, Shengran Hu, Jakob Foerster, David Ha, and Jeff Clune. 2026. “Towards end-to-end automation of AI research.” Nature 651 (8107): 914–19. https://doi.org/10.1038/s41586-026-10265-5.

Lu, Xun, and Halbert White. 2014. “Robustness Checks and Robustness Tests in Applied Economics.” Journal of Econometrics 178 (Part 1): 194–206. https://doi.org/10.1016/j.jeconom.2013.08.016.

Marcus, George E., Michael MacKuen, and W. Russell Neuman. 2011. “Parsimony and Complexity: Developing and Testing Theories of Affective Intelligence.” Political Psychology 32 (2): 323–36. https://doi.org/10.1111/j.1467-9221.2010.00806.x.

Marcus, George E., W. Russell Neuman, and Michael MacKuen. 2000. Affective Intelligence and Political Judgment. Chicago: University of Chicago Press.

Martin, Robert C. 2002. Agile Software Development: Principles, Patterns, and Practices. Upper Saddle River, NJ: Prentice Hall.

———. 2009. Clean Code: A Handbook of Agile Software Craftsmanship. Upper Saddle River, NJ: Prentice Hall.

———. 2018. Clean Architecture: A Craftsman’s Guide to Software Structure and Design. Boston, MA: Prentice Hall.

Maslej, Nestor, Loredana Fattorini, Raymond Perrault, Yolanda Gil, Vanessa Parli, Njenga Kariuki, Emily Capstick, et al. 2025. “The AI Index 2025 Annual Report.” https://hai.stanford.edu/ai-index/2025-ai-index-report.

Mellon, Jonathan. 2025. “Rain, rain, go away: 194 potential exclusion-restriction violations for studies using weather as an instrumental variable.” American Journal of Political Science 69 (3): 881–98. https://doi.org/10.1111/ajps.12894.

Merton, Robert K. 1968. “The Matthew Effect in Science: The reward and communication systems of science are considered.” Science 159 (3810): 56–63. https://doi.org/10.1126/science.159.3810.56.

Messing, Solomon, and Josh Tucker. 2026. “The Train Has Left the Station: Agentic AI and the Future of Social Science Research.” Brookings. https://www.brookings.edu/articles/the-train-has-left-the-station-agentic-ai-and-the-future-of-social-science-research/.

METR. 2026. “Time Horizon 1.1.” https://metr.org/blog/2026-1-29-time-horizon-1-1/. https://metr.org/blog/2026-1-29-time-horizon-1-1/.

Milgrom, Paul R. 1981. “Good News and Bad News: Representation Theorems and Applications.” The Bell Journal of Economics 12 (2): 380–91. https://doi.org/10.2307/3003562.

Mohammadi, Ehsan, Mike Thelwall, Yizhou Cai, Taylor Collier, Iman Tahamtan, and Azar Eftekhar. 2026. “Is Generative AI Reshaping Academic Practices Worldwide? A Survey of Adoption, Benefits, and Concerns.” Information Processing & Management 63 (1): 104350. https://doi.org/10.1016/j.ipm.2025.104350.

Mollick, Ethan. 2024. Co-Intelligence. Random House UK.

Moss-Racusin, Corinne A., John F. Dovidio, Victoria L. Brescoll, Mark J. Graham, and Jo Handelsman. 2012. “Science faculty’s subtle gender biases favor male students.” Proceedings of the National Academy of Sciences 109 (41): 16474–79. https://doi.org/10.1073/pnas.1211286109.

Munafò, Marcus R, Brian A Nosek, Dorothy VM Bishop, Katherine S Button, Christopher D Chambers, Nathalie Percie du Sert, Uri Simonsohn, Eric-Jan Wagenmakers, Jennifer J Ware, and John PA Ioannidis. 2017. “A manifesto for reproducible science.” Nature Human Behaviour 1 (1): 0021. https://doi.org/10.1038/s41562-016-0021.

Munger, Kevin. 2023. “Temporal Validity as Meta-Science.” Research & Politics 10 (3). https://doi.org/10.1177/205316802311872.

———. 2026. “Peer Review 2027.” Substack blog post. https://kevinmunger.substack.com/p/peer-review-2027.

Munger, Kevin, Bert N. Bakker, Adam J. Berinsky, Natascha Just, Andrew Markus Guess, Nathalie Giger, Keren Tenenboim-Weinblatt, Regina Lawrence, and Arnout van de Rijt. 2026. “Peer Review 2027: Scenarios for Academic Publishing in the Age of AI,” January. https://doi.org/10.31235/osf.io/594zj\_v1.

Munger, Kevin, Andrew M Guess, and Eszter Hargittai. 2021. “Quantitative Description of Digital Media: A Modest Proposal to Disrupt Academic Publishing.” Journal of Quantitative Description: Digital Media 1. https://doi.org/10.51685/jqd.2021.000.

Nakano, Reiichiro, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, et al. 2022. “WebGPT: Browser-assisted question-answering with human feedback.” https://arxiv.org/abs/2112.09332.

Narechania, Tejas N. 2022. “Machine Learning as Natural Monopoly.” Iowa Law Review 107: 1543–1614. https://doi.org/10.2139/ssrn.3810366.

Nosek, Brian A, and Timothy M Errington. 2020. “What is replication?” PLoS Biology 18 (3): e3000691. https://doi.org/10.1371/journal.pbio.3000691.

Noy, Shakked, and Whitney Zhang. 2023. “Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence.” Science 381 (6654): 187–92. https://doi.org/10.1126/science.adh2586.

O’Laughlin, Doug, Jeremie Eliahou Ontiveros, Jordan Nanos, Dylan Patel, and Daniel Nishball. 2026. “Claude Code Is the Inflection Point.” SemiAnalysis. February 5, 2026. https://web.archive.org/web/20260430141617/https://newsletter.semianalysis.com/p/claude-code-is-the-inflection-point.

Offer-Westort, Molly, Jiehan Liu, Nick Feamster, Kartik Garg, Nguyen Phong Hoang, and Sudhamshu Hosamane. 2026. “Deep Canvassing with Automated Conversational Agents: Personalized Messaging to Change Attitudes.” Research & Politics 13 (1): 1–14. https://doi.org/10.1177/20531680251414926.

Olken, Benjamin A. 2015. “Promises and Perils of Pre-Analysis Plans.” Journal of Economic Perspectives 29 (3): 61–80. https://doi.org/10.1257/jep.29.3.61.

Ollion, Étienne, Rubing Shen, Ana Macanovic, and Arnault Chatelain. 2024. “The dangers of using proprietary LLMs for research.” Nature Machine Intelligence 6 (January): 4–5. https://doi.org/10.1038/s42256-023-00783-6.

Open Science Collaboration. 2015. “Estimating the Reproducibility of Psychological Science.” Science 349 (6251): aac4716. https://doi.org/10.1126/science.aac4716.

OpenAI. 2026. “ChatGPT for Data Science & Analytics.” 2026. https://web.archive.org/web/20260330095922/https://chatgpt.com/business/ai-for-data-science-analytics/.

Oprysko, Caitlin. 2025. “An explosion of AI lobbying.” Politico. https://www.politico.com/newsletters/politico-influence/2025/07/23/ai-lobbying-explosion-00472092.

Otis, Nicholas, Solène Delecourt, Katelyn Cranney, and Rembrand Koning. 2024. “Global evidence on gender gaps and generative AI.” Harvard Business School Working Paper.

Oxford University Press. 2024. “Researchers and AI Survey Findings.” Oxford University Press. https://pages.oup.com/he/us/ai-survey.

Palmer, Alexis, Noah A. Smith, and Arthur Spirling. 2024. “Using Proprietary Language Models in Academic Research Requires Explicit Justification.” Nature Computational Science. https://doi.org/10.1038/s43588-023-00585-1.

Panizza, Folco, Yara Kyrychenko, and Jon Roozenbeek. 2026. “Survey-taking AI tools surpass human abilities. Here’s what we can do about it.” Nature 650 (8101): 293–95. https://doi.org/10.1038/d41586-026-00386-2.

Parashar, Manish, Tess DeBlanc-Knowles, Erwin Gianchandani, and Lynne E. Parker. 2023. “Strengthening and Democratizing Artificial Intelligence Research and Development.” Computer 56 (11): 85–90. https://doi.org/10.1109/mc.2023.3284568.

Parthasarathy, Ramya, Vijayendra Rao, and Nethra Palaniswamy. 2019. “Deliberative Democracy in an Unequal World: A Text-As-Data Study of South India’s Village Assemblies.” American Political Science Review 113 (3): 623–40. https://doi.org/10.1017/s0003055419000182.

Patel, Dylan, AJ Kourabi, Doug O’Laughlin, and Reyk Knuhtsen. 2025. “DeepSeek Debates: Chinese Leadership On Cost, True Training Cost, Closed Model Margin Impacts.” https://newsletter.semianalysis.com/p/deepseek-debates.

Patwardhan, Tejal, Rachel Dias, Elizabeth Proehl, Grace Kim, Michele Wang, Olivia Watkins, Simón Posada Fishman, et al. 2025. “GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks.” arXiv. https://doi.org/10.48550/arXiv.2510.04374.

Pepinsky, Thomas B. 2026. “Agentic AI and Social Science Research Practice.” Blog post. https://tompepinsky.com/2026/01/23/agentic-ai-and-social-science-research-practice/.

Pichai, Sundar. 2026. “Cloud Next ‘26: Momentum and Innovation at Google Scale.” Google. April 22, 2026. https://web.archive.org/web/20260425012108/https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/cloud-next-2026-sundar-pichai/.

Poldrack, Russell. 2025. “AI-assisted Coding: 10 Simple Rules to Maintain Scientific Rigor.” The Transmitter. https://doi.org/10.53053/LCDN3424.

Politzer-Ahles, Stephen, Teresa Girolamo, and Samantha Ghali. 2020. “Preliminary evidence of linguistic bias in academic reviewing.” Journal of English for Academic Purposes 47: 100895. https://doi.org/10.1016/j.jeap.2020.100895.

Popper, Karl R. 1963. Conjectures and refutations: The growth of scientific knowledge. London: Routledge; Kegan Paul.

Prallon, Brenda. 2026. “How Robust are Robustness Checks?” https://arxiv.org/abs/2602.19384.

Prather, James, Brent N. Reeves, Juho Leinonen, Stephen MacNeil, Arisoa S. Randrianasolo, Brett A. Becker, Bailey Kimmel, Jared Wright, and Ben Briggs. 2024. “The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers.” In Proceedings of the ACM Conference on International Computing Education Research. https://doi.org/10.1145/3632620.3671116.

Refine. 2026. “Refine.” Website. https://refine.sh/.

Reif, Jessica A., Richard P. Larrick, and Jack B. Soll. 2025. “Evidence of a social evaluation penalty for using AI.” Proceedings of the National Academy of Sciences 122 (19). https://doi.org/10.1073/pnas.2426766122.

Requarth, Tim. 2025. “From Bench to Bot: Why AI-powered Writing May Not Deliver on Its Promise.” The Transmitter. https://doi.org/10.53053/HZQR1694.

Reuben, Ernesto, Paola Sapienza, and Luigi Zingales. 2014. “How stereotypes impair women’s careers in science.” Proceedings of the National Academy of Sciences 111 (12): 4403–8. https://doi.org/10.1073/pnas.1314788111.

Root, William B., and Ruth Anne Rehfeldt. 2021. “Towards a Modern-Day Teaching Machine: The Synthesis of Programmed Instruction and Online Education.” The Psychological Record 71 (1): 85–94. https://doi.org/10.1007/s40732-020-00415-0.

Rozado, David. 2024. “The political preferences of LLMs.” PLOS ONE 19 (7): 1–15. https://doi.org/10.1371/journal.pone.0306621.

Ryder, Norman B. 1965. “The Cohort as a Concept in the Study of Social Change.” American Sociological Review 30 (6): 843. https://doi.org/10.2307/2090964.

Scheidel, Walter. 2024. “Beyond technology and wages: power and the history of inequality.” Oxford Open Economics 3 (Supplement_1): i212–16. https://doi.org/10.1093/ooec/odad010.

Schmidgall, Samuel, Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu, Jiang Liu, Michael Moor, Zicheng Liu, and Emad Barsoum. 2025. “Agent Laboratory: Using LLM Agents as Research Assistants.” arXiv preprint.

Shah, Syed Mehtab Hussain, Frank Hopfgartner, and Arnim Bleier. 2026. “Automating Computational Reproducibility in Social Science: Comparing Prompt-Based and Agent-Based Approaches.” arXiv preprint. https://doi.org/10.48550/arXiv.2602.08561.

Shaw, Steven D, and Gideon Nave. 2026. “Thinking–Fast, Slow, and Artificial: How AI Is Reshaping Human Reasoning and the Rise of Cognitive Surrender.” January 12, 2026. https://doi.org/10.31234/osf.io/yk25n_v1.

Shen, Judy Hanwen, and Alex Tamkin. 2026. “How AI Impacts Skill Formation.” February 1, 2026. https://doi.org/10.48550/arXiv.2601.20245.

Siebert, Luciano Cavalcante, Maria Luce Lupetti, Evgeni Aizenberg, Niek Beckers, Arkady Zgonnikov, Herman Veluwenkamp, David Abbink, et al. 2023. “Meaningful Human Control: Actionable Properties for AI System Development.” AI and Ethics 3: 241–55. https://doi.org/10.1007/s43681-022-00167-3.

Simmons, Joseph P., Leif D. Nelson, and Uri Simonsohn. 2011. “False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant.” Psychological Science 22 (11): 1359–66. https://doi.org/10.1177/0956797611417632.

Simon, Herbert A. 1947. Administrative Behavior. New York: Macmillan.

Simonsohn, Uri, Joseph P. Simmons, and Leif D. Nelson. 2020. “Specification Curve Analysis.” Nature Human Behaviour 4 (11): 1208–14. https://doi.org/10.1038/s41562-020-0912-z.

Sleegers, Willem, and Jamie Elsey. 2025. “Adoption and Uses of LLMs among U.S. Tech Workers.” San Francisco: Rethink Priorities. https://rethinkpriorities.org/wp-content/uploads/2025/07/LLM-industry-survey.pdf.

Smith, Aaron, and Monica Anderson. 2018. “Social Media Use in 2018.” Pew Research Center. https://www.pewresearch.org/internet/2018/03/01/social-media-use-in-2018/.

Soderstrom, Nicholas C., and Robert A. Bjork. 2015. “Learning versus Performance: An Integrative Review.” Perspectives on Psychological Science 10 (2): 176–99. https://doi.org/10.1177/1745691615569000.

Sparrow, Betsy, Jenny Liu, and Daniel M. Wegner. 2011. “Google Effects on Memory: Cognitive Consequences of Having Information at Our Fingertips.” Science 333 (6043): 776–78. https://doi.org/10.1126/science.1207745.

Spector-Bagdady, Kayte. 2025. “The Need for Prospective Integrity Standards for the Use of Generative AI in Research.” The Journal of Law, Medicine & Ethics 53 (1). https://doi.org/10.1017/jme.2025.41.

Spencer, Steven J., Claude M. Steele, and Diane M. Quinn. 1999. “Stereotype Threat and Women’s Math Performance.” Journal of Experimental Social Psychology 35 (1): 4–28. https://doi.org/10.1006/jesp.1998.1373.

Spirling, Arthur. 2023. “Why open-source generative AI models are an ethical way forward for science.” Nature 616: 413. https://doi.org/10.1038/d41586-023-01295-4.

———. 2026. “2025–2026 Talks on Replication, Open Models, and Social-Science Uses of Language Models.” Talks at Stanford Political Science and Yale ISPS.

Stanford Institute for Human-Centered Artificial Intelligence. 2026. “Artificial Intelligence Index Report 2026.” Stanford Institute for Human-Centered Artificial Intelligence. https://hai.stanford.edu/assets/files/ai\%5Findex\%5Freport\%5F2026.pdf.

Steegen, Sara, Francis Tuerlinckx, Andrew Gelman, and Wolf Vanpaemel. 2016. “Increasing Transparency Through a Multiverse Analysis.” Perspectives on Psychological Science 11 (5): 702–12. https://doi.org/10.1177/1745691616658637.

Stewart, Rebecca. 2025. “Hi! It’s Duo: The Marketing Strategy Behind the Internet’s Favorite Green Menace.” Adweek, April 8, 2025. https://web.archive.org/web/20250425210150/https://www.adweek.com/brand-marketing/duolingo-duo-owl-marketing-strategy/.

Stockemer, Daniel, Gabriela Galassi, and Engi Abou-El-Kheir. 2026. “A fresh look at the publication and citation gap between men and women: insights from economics and political science.” Humanities and Social Sciences Communications, March. https://doi.org/10.1057/s41599-026-06786-z.

Stuhler, Oscar, Cat Dang Ton, and Étienne Ollion. 2025. “From Codebooks to Promptbooks: Extracting Information from Text with Generative Large Language Models.” Sociological Methods & Research. https://doi.org/10.1177/00491241251336794.

Tang, Chuang, Shaobo (Kevin) Li, Suming Hu, Fue Zeng, and Qianzhou Du. 2025. “Gender disparities in the impact of generative artificial intelligence: Evidence from academia.” PNAS Nexus 4 (2): pgae591. https://doi.org/10.1093/pnasnexus/pgae591.

Tankelevitch, Lev, Viktor Kewenig, Auste Simkute, Ava Elizabeth Scott, Advait Sarkar, Abigail Sellen, and Sean Rintel. 2024. “The Metacognitive Demands and Opportunities of Generative AI.” In Proceedings of the CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3613904.3642902.

Tetenov, Aleksey. 2016. “An Economic Theory of Statistical Testing.” CeMMAP Working Paper CWP50/16. Centre for Microdata Methods; Practice, Institute for Fiscal Studies. https://doi.org/10.1920/wp.cem.2016.5016.

Than, Ker. 2026. “Social Scientists Embrace the AI Moment.” Stanford Report. April 24, 2026. https://news.stanford.edu/stories/2026/04/ai-social-science-empirical-research.

Thapa, Surendrabikram, Shuvam Shiwakoti, Siddhant Bikram Shah, Surabhi Adhikari, Hariram Veeramani, Mehwish Nasim, and Usman Naseem. 2025. “Large Language Models (LLM) in Computational Social Science: Prospects, Current State, and Challenges.” Social Network Analysis and Mining 15 (1): 4. https://doi.org/10.1007/s13278-025-01428-9.

The Polarization Research Lab. 2026. “The Polarization Research Lab.” Website. https://polarizationresearchlab.org/.

Thomas, Kathrin. 2024. “The Advent of Survey Experiments in Politics and International Relations.” Government and Opposition 59 (1): 297–320. https://doi.org/10.1017/gov.2022.36.

Thomas, Llewellyn D. W., Angelo Kenneth G. Romasanta, and Laia Pujol Priego. 2026. “Jagged Competencies: Measuring the Reliability of Generative AI in Academic Research.” Journal of Business Research. https://doi.org/10.1016/j.jbusres.2025.115804.

Thompson, Clive. 2026. “Coding after Coders: The End of Computer Programming as We Know It.” The New York Times Magazine, March 12, 2026. https://www.nytimes.com/2026/03/12/magazine/ai-coding-programming-jobs-claude-chatgpt.html.

Tong, Anna, Kenrick Cai, and Krystal Hu. 2025. “Exclusive: Google, Scale AI’s largest customer, plans split after Meta deal, sources say.” Reuters. https://www.reuters.com/business/google-scale-ais-largest-customer-plans-split-after-meta-deal-sources-say-2025-06-13/.

Tripodi, Giorgio, Xiang Zheng, Yifan Qian, Dakota Murray, Benjamin F. Jones, Chaoqun Ni, and Dashun Wang. 2025. “Tenure and research trajectories.” Proceedings of the National Academy of Sciences 122 (30). https://doi.org/10.1073/pnas.2500322122.

Tsvetkova, Milena, Taha Yasseri, Niccolo Pescetelli, and Tobias Werner. 2024. “A New Sociology of Humans and Machines.” Nature Human Behaviour 8 (10): 1864–76. https://doi.org/10.1038/s41562-024-02001-8.

Twyman, Janet S. 2020. “Programmed Instruction.” In The Encyclopedia of Child and Adolescent Development, edited by Stephen Hupp and Jeremy Jewell, 1–11. Wiley. https://doi.org/10.1002/9781119171492.wecad095.

Uprichard, Emma, Roger Burrows, and David Byrne. 2008. “SPSS as an ’Inscription Device’: From Causality to Description?” The Sociological Review 56 (4): 606–22. https://doi.org/10.1111/j.1467-954x.2008.00807.x.

Van Noorden, Richard, and Jeffrey M. Perkel. 2023. “AI and science: what 1, 600 researchers think.” Nature 621 (7980): 672–75. https://doi.org/10.1038/d41586-023-02980-0.

Velez, Yamil R., Donald P. Green, and Semra Sevi. 2025. “Chatbot Voting Advice Applications inform but seldom sway young unaligned voters.” Proceedings of the National Academy of Sciences 122 (50): e2515516122. https://doi.org/10.1073/pnas.2515516122.

Vendraminelli, Luca, Matthew DosSantos DiSorbo, Annika Hildebrandt, Edward McFowland III, Arvind Karunakaran, and Iavor Bojinov. 2025. “The GenAI Wall Effect: Examining the Limits to Horizontal Expertise Transfer Between Occupational Insiders and Outsiders.” https://doi.org/10.2139/ssrn.5462694.

Vernon, Vaughn. 2013. Implementing Domain-Driven Design. Upper Saddle River, NJ: Addison-Wesley.

Voelkel, Jan G, Michael N Stagnaro, James Y Chu, Sophia L Pink, Joseph S Mernyk, Chrystal Redekopp, Isaias Ghezae, et al. 2024. “Megastudy testing 25 treatments to reduce antidemocratic attitudes and partisan animosity.” Science 386 (6719): eadh4764. https://doi.org/10.1126/science.adh4764.

Voteview. 2026. “Voteview.” Website. https://voteview.com/.

Wald, Abraham. 1950. Statistical Decision Functions. New York: John Wiley & Sons. https://books.google.com/books?id=nq0gAAAAMAAJ.

Wallace, Michael, and Arne L. Kalleberg. 1982. “Industrial Transformation and the Decline of Craft: The Decomposition of Skill in the Printing Industry, 1931-1978.” American Sociological Review 47 (3): 307. https://doi.org/10.2307/2094988.

Wang, Chenglong, Haoyu Tang, Xiyuan Yang, Yueqi Xie, Jina Suh, Sunayana Sitaram, Junming Huang, et al. 2025. “Uncovering inequalities in new knowledge learning by large language models across different languages.” Proceedings of the National Academy of Sciences 122 (51). https://doi.org/10.1073/pnas.2514626122.

Watters, Audrey. 2021. Teaching Machines: The History of Personalized Learning. The MIT Press. https://doi.org/10.7551/mitpress/12262.001.0001.

Wenger, Emily, and Yoed N Kenett. 2026. “Large language models are homogeneously creative.” PNAS Nexus 5 (3): pgag042. https://doi.org/10.1093/pnasnexus/pgag042.

Westwood, Sean J. 2025. “The potential existential threat of large language models to online survey research.” Proceedings of the National Academy of Sciences 122 (47). https://doi.org/10.1073/pnas.2518075122.

Westwood, Sean J., and Samuel Frederick. 2026. “Reply to Van der Stigchel et al.: Empirical evidence that AI survey contamination is real and substantial.” Proceedings of the National Academy of Sciences 123 (8). https://doi.org/10.1073/pnas.2537420123.

White, Halbert. 2000. “A Reality Check for Data Snooping.” Econometrica 68 (5): 1097–1126. https://doi.org/10.1111/1468-0262.00152.

Wiley. 2025. “AI Adoption Jumps to 84% among Researchers as Expectations Undergo Significant Reality Check.” Press release. https://newsroom.wiley.com/press-releases/press-release-details/2025/AI-Adoption-Jumps-to-84-Among-Researchers-as-Expectations-Undergo-Significant-Reality-Check/default.aspx.

Wu, Patrick Y. 2026. “Beyond Price: A Technical Quality Framework for AI Antitrust.” SSRN Working Paper. https://papers.ssrn.com/sol3/papers.cfm?abstract\%5Fid=6078646.

Wu, Patrick Y., Jonathan Nagler, Joshua A. Tucker, and Solomon Messing. 2023. “Large Language Models Can Be Used to Estimate the Latent Positions of Politicians.” https://arxiv.org/abs/2303.12057.

Xiao, Tianqi, Francesco Fuso Nerini, H. Damon Matthews, Massimo Tavoni, and Fengqi You. 2025. “Environmental Impact and Net-Zero Pathways for Sustainable Artificial Intelligence Servers in the USA.” Nature Sustainability 8: 1541–53. https://doi.org/10.1038/s41893-025-01681-y.

Xu, Haotian, and Wenqin Shen. 2026. “From tool to partner: generative AI usage patterns and research performance among doctoral students.” Studies in Higher Education, 1–16. https://doi.org/10.1080/03075079.2026.2625386.

Xu, Ruoxi, Yingfei Sun, Mengjie Ren, Shiguang Guo, Ruotong Pan, Hongyu Lin, Le Sun, and Xianpei Han. 2024. “AI for Social Science and Social Science of AI: A Survey.” Information Processing & Management 61 (3): 103665. https://doi.org/10.1016/j.ipm.2024.103665.

Xu, Yiqing, and Leo Yang Yang. 2026. “Scaling Reproducibility: An AI-Assisted Workflow for Large-Scale Replication and Reanalysis.” https://arxiv.org/abs/2602.16733.

Zeff, Maxwell. 2026. “How Claude Code Is Reshaping Software–and Anthropic.” Wired, January 22, 2026. https://www.wired.com/story/claude-code-success-anthropic-business-model/.

Zhang, Jiaxing, and Shuaishuai Feng. 2021. “Machine learning modeling: A new way to do quantitative research in social sciences in the era of AI.” Journal of Web Engineering 20 (2): 281–302. https://doi.org/10.13052/jwe1540-9589.2023.

Zhang, Simone, Janet Xu, and AJ Alvero. 2025. “Generative AI Meets Open-Ended Survey Responses: Research Participant Use of AI and Homogenization.” Sociological Methods & Research 54 (3): 1197–1242. https://doi.org/10.1177/00491241251327130.

Zhang, Yongjun. 2025. “Generative AI has lowered the barriers to computational social sciences.” https://arxiv.org/abs/2311.10833.

———. 2026. “Vibe Researching as Wolf Coming: Can AI Agents with Skills Replace or Augment Social Scientists?” arXiv. https://doi.org/10.48550/ARXIV.2602.22401.

Zhou, Xuhui, Hao Zhu, Leena Mathur, Ruohong Zhang, Haofei Yu, Zhengyang Qi, Louis-Philippe Morency, et al. 2024. “SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents.” https://arxiv.org/abs/2310.11667.

Ziems, Caleb, William Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang, and Diyi Yang. 2024. “Can Large Language Models Transform Computational Social Science?” Computational Linguistics 50 (1): 237–91. https://doi.org/10.1162/coli_a_00502.