Trackademia

Welcome to Trackademia

The idea behind Trackademia is to build on existing academic databases like Google Scholar and OpenAlex so that you can trust the data and use it for whatever you want. That comes down to two things.

More data, and more ways to use it. We aggregate publication data from every major source into a single dataset and add information pulled from faculty CVs. Then we let you slice it however you want: choose which sources to build from, write your own formulas, weight journals, adjust for department size.

More accurate, and honest about where it isn’t. We focus on a smaller set of schools so we can verify everything carefully. Faculty lists come from department websites rather than being inferred from publication records, so we know who should be in the data before we go looking. When we can’t find data on someone, we record the gap instead of dropping them. Every number comes with a coverage rate you can check against ground truth.

Everything in Trackademia is built from seven units. AI merges duplicate records across sources and tags each publication so these categories hold up:

  • Publication — citation counts, authors, journal, peer review status, and more
  • Publication group — versions of the same work combined into one entity, such as a working paper and the article it became
  • Academic
  • Department
  • University
  • Field
  • Journal

There are two modes. In Database mode you browse the underlying records and filter by any of the seven units. In Analytics mode you get graphs and comparisons, and you switch views to compare two departments, compare one department against the average of the others, or see them all at once. Tabs let you focus on a particular level of the data.
Trackademia
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Academic

Arthur Spirling

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Academic

RankDistinguished
As of2025 · CV
DepartmentPrinceton Politics
UniversityPrinceton
FieldPolitical Science
Scholarprofile

Totals

Publications11
Citations1,015
h-index30 · Scholar
Citations per year in post56.4
Years in post18 from the CV positions

Coverage

Works with a count10 of 11
Share91%

Positions

Director of the Center for Statistics and Mac…2025–
Director of Graduate Studies of Politics, Pri…2024–2025
Class of 1987 Professor of Politics, Princeto…2023–
Professor of Politics and Data Science, New Y…2019–2023
Chair, Steering Committee, Moore-Sloan Data S…2018–2022
Deputy Director and Director of Graduate Stud…2016–2022
Associate Professor of Politics and Data Scie…2015–2019
Chair, Education Working Group, Moore-Sloan D…2015–2022
and 3 more

Top venues 9 venues

The 8 holding the most of these works; 1 more hold at least one.

Political Analysis2
The Journal of Politics2
American Journal of Political Science1
American Political Science Review1
Journal of the American Statistical Associati…1
Nature1
Nature Computational Science1
Nature Human Behaviour1

Publications per year

Works this academic published in each year, by the year each work appeared; an undated work is in no year, and a work two colleagues wrote counts for each of them. The kind, works and year filters decide which works are here at all.

Citations per year

Citations received in each year, from this academic’s verified Scholar profile, which counts every citation to everything they ever wrote: no publication filter reaches this line - not the kinds, not the works switch, not the year window on publication - and no journal weight either. It is a profile total, so it does not match the works listed below.

Publications 11 works

Every work these filters select, most cited first and the works with no count at the end: a work nobody has a figure for is unknown, never a zero to be ranked among the works that have one, and it prints an em dash. Each figure is that work’s own citations - Scholar’s count where there is one, else OpenAlex’s - and is not weighted.

YearTitleVenueKindCitations
2022Word Embeddings: What works, what doesn’t, and how to tell the difference for applied researchThe Journal of Politicsarticle395
2023Open generative AI models are a way forward for scienceNaturearticle206
2023Embedding Regression: Models for Context-Specific Description and InferenceAmerican Political Science Reviewarticle159
2023Using proprietary language models in academic research requires explicit justificationNature Computational Sciencearticle93
2024Large Language Models Can Argue in Convincing Ways About Politics, But Humans Dislike AI Authors: implications for GovernancePolitical Sciencearticle75
2025What Good is a Regression? Inference to the Best Explanation and the Practice of Political Science ResearchThe Journal of Politicsarticle40
2026A reporting checklist for large language models in behavioural scienceNature Human Behaviourarticle19
2024Multilanguage Word Embeddings For Social Science: Estimation, Inference and Validation Resources for 157 LanguagesPolitical Analysisarticle18
2025Measuring Distances in High Dimensional Spaces Why Average Group Vector Comparisons Exhibit Bias, And What to Do About itPolitical Analysisarticle7
2025Peers, Equals, and Jurors: New Data and Methods on the Role of Equality in Leveller Thought.American Journal of Political Sciencearticle3
—Identifying Intra-Party Voting Blocs in the UK House of CommonsJournal of the American Statistical Associationarticle—