John J. Nay

617 total citations
26 papers, 241 citations indexed

About

John J. Nay is a scholar working on Political Science and International Relations, Sociology and Political Science and Artificial Intelligence. According to data from OpenAlex, John J. Nay has authored 26 papers receiving a total of 241 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Political Science and International Relations, 6 papers in Sociology and Political Science and 6 papers in Artificial Intelligence. Recurrent topics in John J. Nay's work include Artificial Intelligence in Law (7 papers), Evolutionary Game Theory and Cooperation (4 papers) and Remote Sensing in Agriculture (3 papers). John J. Nay is often cited by papers focused on Artificial Intelligence in Law (7 papers), Evolutionary Game Theory and Cooperation (4 papers) and Remote Sensing in Agriculture (3 papers). John J. Nay collaborates with scholars based in United States, India and Philippines. John J. Nay's co-authors include Jonathan M. Gilligan, Kam Leung Yeung, Thushara Gunda, Daniel B. Gallagher, David J. Hess, Helena Wright, Mark Abkowitz, Eric Chu, George M. Hornberger and Scott C. Worland and has published in prestigious journals such as Science, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

John J. Nay

26 papers receiving 230 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
John J. Nay United States	10	49	47	44	34	30	26	241
Anna Vári Hungary	12	29 0.6×	32 0.7×	128 2.9×	158 4.6×	28 0.9×	45	368
Jawoto Sih Setyono Indonesia	10	35 0.7×	11 0.2×	85 1.9×	93 2.7×	40 1.3×	41	350
Adjie Pamungkas Indonesia	10	10 0.2×	28 0.6×	56 1.3×	72 2.1×	9 0.3×	72	336
C. Els van Daalen Netherlands	5	48 1.0×	12 0.3×	78 1.8×	37 1.1×	15 0.5×	10	225
Deasy Arisanty Indonesia	11	16 0.3×	29 0.6×	69 1.6×	77 2.3×	12 0.4×	145	437
Rianne Bijlsma Netherlands	5	35 0.7×	7 0.1×	118 2.7×	55 1.6×	12 0.4×	10	268
Stefano Farolfi France	13	54 1.1×	11 0.2×	64 1.5×	50 1.5×	117 3.9×	52	391
Nicola Isendahl Germany	6	45 0.9×	8 0.2×	148 3.4×	43 1.3×	19 0.6×	9	312
Nilanjan Ghosh India	9	36 0.7×	6 0.1×	45 1.0×	69 2.0×	33 1.1×	26	251

Countries citing papers authored by John J. Nay

Since Specialization

Citations

This map shows the geographic impact of John J. Nay's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by John J. Nay with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John J. Nay more than expected).

Fields of papers citing papers by John J. Nay

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John J. Nay. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by John J. Nay. The network helps show where John J. Nay may publish in the future.

Co-authorship network of co-authors of John J. Nay

This figure shows the co-authorship network connecting the top 25 collaborators of John J. Nay. A scholar is included among the top collaborators of John J. Nay based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with John J. Nay. John J. Nay is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Nay, John J., et al.. (2024). Large language models as tax attorneys: a case study in legal capabilities emergence. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences. 382(2270). 12 indexed citations

Nay, John J.. (2023). Large Language Models as Fiduciaries: A Case Study Toward Robustly Communicating With Artificial Intelligence Through Legal Standards. SSRN Electronic Journal. 6 indexed citations

Nay, John J.. (2023). Large Language Models as Corporate Lobbyists. SSRN Electronic Journal. 8 indexed citations

Nay, John J., et al.. (2023). Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence. SSRN Electronic Journal. 16 indexed citations

Gervais, Daniel J. & John J. Nay. (2023). Artificial intelligence and interspecific law. Science. 382(6669). 376–378. 2 indexed citations

Nay, John J.. (2022). Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans. SSRN Electronic Journal. 11 indexed citations

Nay, John J. & Katherine J. Strandburg. (2019). Generalizability: Machine Learning and Humans-in-the-Loop. SSRN Electronic Journal. 2 indexed citations

Gilligan, Jonathan M., et al.. (2018). Urban Water Conservation Policies in the United States. Earth s Future. 6(7). 955–967. 14 indexed citations

Nay, John J., et al.. (2018). Topic Modeling the President. 1 indexed citations

10.

Nay, John J.. (2018). Natural Language Processing and Machine Learning for Law and Policy Texts. SSRN Electronic Journal. 14 indexed citations

11.

Nay, John J.. (2017). Predicting and understanding law-making with word vectors and an ensemble model. PLoS ONE. 12(5). e0176999–e0176999. 16 indexed citations

12.

Nay, John J., et al.. (2017). Topic Modeling the President: Conventional and Computational Methods. SSRN Electronic Journal. 86. 1243. 7 indexed citations

13.

Nay, John J., Emily Burchfield, & Jonathan M. Gilligan. (2016). Forecasting Vegetation Health at High Spatial Resolution. arXiv (Cornell University). 2 indexed citations

14.

Nay, John J. & Yevgeniy Vorobeychik. (2016). Predicting Human Cooperation. PLoS ONE. 11(5). e0155656–e0155656. 6 indexed citations

15.

Burchfield, Emily, John J. Nay, & Jonathan M. Gilligan. (2016). APPLICATION OF MACHINE LEARNING TO THE PREDICTION OF VEGETATION HEALTH. The international archives of the photogrammetry, remote sensing and spatial information sciences. XLI-B2. 465–469. 3 indexed citations

16.

Nay, John J. & Jonathan M. Gilligan. (2015). Data-driven dynamic decision models. arXiv (Cornell University). 2752–2763. 1 indexed citations

17.

Gilligan, Jonathan M., Corey Brady, Janey Camp, John J. Nay, & Pratim Sengupta. (2015). Participatory simulations of urban flooding for learning and decision support. 2015 Winter Simulation Conference (WSC). 3174–3175. 3 indexed citations

18.

Nay, John J.. (2014). PREDICTING COOPERATION AND DESIGNING INSTITUTIONS: AN INTEGRATION OF BEHAVIORAL DATA, MACHINE LEARNING, AND SIMULATION. 2 indexed citations

19.

Nay, John J., Christine O. Menias, Vincent M. Mellnick, & Dennis M. Balfe. (2014). Gastrointestinal manifestations of systemic disease: a multimodality review. Abdominal Imaging. 40(6). 1926–1943. 17 indexed citations

20.

Nay, John J., Mark Abkowitz, Eric Chu, Daniel B. Gallagher, & Helena Wright. (2014). A review of decision-support models for adaptation to climate change in the context of development. Climate and Development. 6(4). 357–367. 32 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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