R. Thomas McCoy

68 total papers · 1.1k total citations
18 papers, 332 citations indexed

About

R. Thomas McCoy is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Political Science and International Relations. According to data from OpenAlex, R. Thomas McCoy has authored 18 papers receiving a total of 332 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 1 paper in Political Science and International Relations. Recurrent topics in R. Thomas McCoy's work include Natural Language Processing Techniques (12 papers), Topic Modeling (12 papers) and Text Readability and Simplification (4 papers). R. Thomas McCoy is often cited by papers focused on Natural Language Processing Techniques (12 papers), Topic Modeling (12 papers) and Text Readability and Simplification (4 papers). R. Thomas McCoy collaborates with scholars based in United States, United Kingdom and Switzerland. R. Thomas McCoy's co-authors include Tal Linzen, Dipanjan Das, Emily Pitler, Paul Smolensky, Jianfeng Gao, Robert Frank, Najoung Kim, Ian Tenney, Berlin Chen and Ellie Pavlick and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Current Directions in Psychological Science.

In The Last Decade

R. Thomas McCoy

17 papers receiving 311 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
R. Thomas McCoy	280	56	28	21	15	18	332
Denis Paperno	262 0.9×	54 1.0×	12 0.4×	13 0.6×	11 0.7×	30	319
Aaron Steven White	224 0.8×	37 0.7×	22 0.8×	12 0.6×	22 1.5×	25	302
Jon Gauthier	231 0.8×	61 1.1×	37 1.3×	11 0.5×	7 0.5×	10	267
Najoung Kim	286 1.0×	79 1.4×	34 1.2×	16 0.8×	33 2.2×	18	347
Anna Rogers	296 1.1×	67 1.2×	8 0.3×	8 0.4×	25 1.7×	22	363
John K. Pate	233 0.8×	28 0.5×	24 0.9×	19 0.9×	24 1.6×	16	336
Johannes Bjerva	323 1.2×	42 0.8×	7 0.3×	11 0.5×	23 1.5×	45	356
Nam Khanh Tran	260 0.9×	67 1.2×	11 0.4×	18 0.9×	46 3.1×	15	329
Aurélie Herbelot	206 0.7×	39 0.7×	17 0.6×	26 1.2×	7 0.5×	35	239
Simone Conia	267 1.0×	35 0.6×	11 0.4×	3 0.1×	13 0.9×	25	343

Countries citing papers authored by R. Thomas McCoy

Since Specialization

Citations

This map shows the geographic impact of R. Thomas McCoy'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 R. Thomas McCoy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites R. Thomas McCoy more than expected).

Fields of papers citing papers by R. Thomas McCoy

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by R. Thomas McCoy. 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 R. Thomas McCoy. The network helps show where R. Thomas McCoy may publish in the future.

Co-authorship network of co-authors of R. Thomas McCoy

This figure shows the co-authorship network connecting the top 25 collaborators of R. Thomas McCoy. A scholar is included among the top collaborators of R. Thomas McCoy 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 R. Thomas McCoy. R. Thomas McCoy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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