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

Every graph on this site

Publications

  • Ranking Ranked table
    • Value: Citations, Weighted citations, Publications
    • Measure: Total, Mean per work, Median per work
  • Works published per year Lines over years
    • Measure: Total, Mean per academic, Median per academic
    • Calendar year or years since PhD
    • In each year or cumulative
  • Citations received per year Lines over years
    • Value: Citations, Weighted citations
    • Measure: Total, Mean per work, Median per work, Mean per academic, Median per academic
    • In each year or cumulative
  • Citation distribution Bars
    • Value: Citations, Weighted citations
  • Citations by age of work Lines over years
    • Value: Citations, Weighted citations
    • Measure: Total, Mean per work, Median per work
  • Publication type mix Ranked bars
    • Value: Citations, Weighted citations, Publications
  • Top venues Ranked bars
    • Value: Citations, Weighted citations, Publications

Academics

Departments

  • Ranking Ranked table
    • Value: Citations, Weighted citations, Publications
    • Measure: Total, Mean per work, Median per work, Mean per academic, Median per academic, Formula
  • Publications over time Lines over years
    • Measure: Total, Mean per academic, Median per academic, Formula
    • Calendar year or years since PhD
    • In each year or cumulative
  • Citations over time Lines over years
    • Value: Citations, Weighted citations
    • Measure: Total, Mean per academic, Median per academic, Formula
    • In each year or cumulative
  • Makeup by tenure track Stacked bars
  • Output by rank Grouped bars
    • Value: Citations, Weighted citations, Publications
    • Measure: Total, Mean per work, Median per work, Mean per academic, Median per academic, Formula
  • Academics ranked against academics Grouped bars
    • Value: Citations, Weighted citations, Publications
    • How many ranks (opens on 5)
  • Years to promotion Grouped bars
  • Journals used Stacked bars
    • Value: Citations, Weighted citations, Publications

Journalshere

Fields

  • Ranking Ranked table
    • Value: Citations, Weighted citations, Publications
    • Measure: Total, Mean per work, Median per work, Mean per academic, Median per academic
  • Departments by field Bars
    • Measure: Total, Mean per work, Median per work, Mean per academic, Median per academic
  • University rollup Ranked bars
  • Coverage by field Ranked bars

Trackademia

Search the whole site

Search a person, a department, a journal or the title of a work. A person’s name brings up their profile, their publications, their department and the journals they publish in.

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Journal

Political Analysis

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Citation weight

Tier2
Weight in force
Weightingoff

Weight inputs

Each figure with its percentile among the ranked journals. A is the median, over the academics with a work here, of that academic’s citations to the works this site counts for them - never a Google Scholar profile total. Over every academic of this field, all years, whatever the filters select.

A · academics’ median6,583.5 · 56%
B · citations per work378.8 · 94%
N · works141 · 87%
P · academics50 · 83%
Formula result1.107

From this institution

Under these filters. A work with no citation count is unknown here, never a zero.

Works79
Citations40,764 over 76 of 79 works
Citations per counted work536.4
Weighted citations
Academics19
Departments8
First seen1991

Works placed per year

78 of these 79 works carry a year; an undated work is in no year. Works placed, under these filters.

Departments publishing there 8

DepartmentWorks
Harvard Government36
Columbia Political Science18
Yale Political Science12
Princeton Politics10
Penn Political Science6
Brown Political Science2
Cornell Government1
Dartmouth Government1

A work held by two departments counts once in each, so these add up to more than the works above.

Works placed there 79

YearTitleAcademicsCitations
2001Logistic Regression in Rare Events DataGary King6,552
2007Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal InferenceGary King6,021
2012Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical TurkGregory Alain Huber5,882
2011Causal Inference Without Balance Checking: Coarsened Exact MatchingGary King5,362
2019Why Propensity Scores Should Not Be Used for MatchingGary King2,813
2014Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference ExperimentsDaniel J. Hopkins2,707
2018Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About ItArthur Spirling913
2015Computer Assisted Text Analysis for Comparative PoliticsDustin Tingley833
2006The Dangers of Extreme CounterfactualsGary King822
2004Bayesian multilevel estimation with poststratification: state-level estimates from national pollsAndrew Gelman663
2014Does Survey Mode Still Matter? Findings from a 2010 Multi-Mode ComparisonStephen Daniel Ansolabehere662
2007Comparing Incomparable Survey Responses: New Tools for Anchoring VignettesGary King594
2012Validation: What Big Data Reveals About Survey Misreporting and the Real ElectorateStephen Daniel Ansolabehere568
2015How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About ItGary King484
2018The Number of Choice Tasks and Survey Satisficing in Conjoint ExperimentsDaniel J. Hopkins373
2005Practical issues in implementing and understanding Bayesian ideal point estimationAndrew Gelman352
2018A Note on Listwise Deletion versus Multiple ImputationThomas B. Pepinsky281
1999No Evidence on Directional vs. Proximity VotingGary King262
2010Bayesian Model Averaging: Theoretical Developments and Practical ApplicationsBrendan Nyhan246
2023Using Conjoint Experiments to Analyze Elections: The Essential Role of the Average Marginal Component EffectDaniel J. Hopkins236
2006Comparing Experimental and Matching Methods using a Large-Scale Voter Mobilization ExperimentAlan S. Gerber · Donald P. Green218
2009The Microfoundations of Polarization.Matthew Levendusky199
2007UK OC OK? Interpreting Optimal Classification Scores for the U.K. House of CommonsArthur Spirling192
2002The Experimental Method in Political ScienceROSE McDERMOTT191
2018Classification Accuracy as a Substantive Quantity of Interest: Measuring Polarization in Westminster SystemsArthur Spirling185
2016The Influence of Emotion on TrustDustin Tingley182
2019A Theory of Statistical Inference for Matching Methods in Causal ResearchGary King178
2001Testing for Publication Bias in Political ScienceAlan S. Gerber · Donald P. Green176
2008Analysis of Cluster-Randomized Experiments: A Comparison of Alternative Estimation ApproachesDonald P. Green162
2004Political Learning from Rare Events: Poisson Inference, Fiscal Constraints, and the Lifetime of BureausDaniel Paul Carpenter156
2009Testing the Accuracy of Regression Discontinuity Analysis Using Experimental BenchmarksAlan S. Gerber · Donald P. Green153
2014Multiple imputation for continuous and categorical data: Comparing joint and conditional ap- proachesAndrew Gelman151
2003Roll Calls, Party Labels, and ElectionsJames M. Snyder, Jr.130
1991On Political MethodologyGary King125
2001Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of DataGary King118
2001Agenda constrained legislator ideal Points and the spatial voting modelAdam Meirowitz116
2013Asking About Numbers: Why and HowStephen Daniel Ansolabehere115
2017Sparse Estimation and Uncertainty with Application to Subgroup AnalysisDustin Tingley107
2003Integrating roll call analysis and voting theory: A frameworkAdam Meirowitz88
2002The Downstream Benefits of ExperimentationAlan S. Gerber · Donald P. Green81
2002A Fast, Easy, and Efficient Estimator for Multiparty Electoral DataGary King78
2010Bayesian combination of state polls and election forecastsAndrew Gelman78
2005Two-stage regression and multilevel modeling: a commentaryAndrew Gelman75
1998An Empirical Spatial Model of Congressional CampaignsNolan McCarty68
2023Statistically Valid Inferences from Differentially Private Data Releases, with Application to the Facebook URLs DatasetGary King66
2003A Consensus on Second Stage Analyses in Ecological Inference ModelsGary King62
2002Reconciling Individual and Aggregate Evidence Concerning Partisan Stability: Applying Time-Series Models to Panel Survey DataDonald P. Green59
2010Baseline, Placebo, and Treatment: Efficient Estimation for Three-Group ExperimentsAlan S. Gerber · Donald P. Green50
2007Bayesian Approaches for Limited Dependent Variable Change Point ProblemsArthur Spirling49
2021Spikes and Variance: Using Google Trends to Detect and Forecast Protests.Erik Wibbels45
2020Voter registration databases and MRP: Toward the use of large scale databases in public opinion researchAndrew Gelman43
2022An Improved Method of Automated Nonparametric Content Analysis for Social ScienceGary King41
2013Preregistration of studies and mock reportsAndrew Gelman39
2000The Time to Give: PAC Motivations and Electoral TimingNolan McCarty34
1993Issues and the Dynamics of Party Identification: A Methodological CritiqueDonald P. Green33
2017Combining Double Sampling and Bounds to Address Non-ignorable Missing Outcomes in Randomized ExperimentsAlan S. Gerber · Donald P. Green30
2003Analyzing Second Stage Ecological RegressionsGary King26
2015Estimating the Severity of the WikiLeaks US Diplomatic Cables DisclosureArthur Spirling26
1996The Generalization in the Generalized Event Count Model, With Comments on Achen, Amato, and LondreganGary King24
2002Interpreting the Coefficient of Party Influence: Comment on KrehbielJames M. Snyder, Jr.24
1991The Effects of Measurement Error on Two-Stage, Least Squares EstimatesDonald P. Green21
1995A Correction for an Underdispersed Event Count Probability DistributionGary King18
2024Multilanguage Word Embeddings For Social Science: Estimation, Inference and Validation Resources for 157 LanguagesArthur Spirling18
2001Party Labels, Roll-Call Votes, and ElectionsJames M. Snyder, Jr.17
G. Porro (2012)“Causal Inference Without Balance Checking: Coarsened Exact Matching.”Gary King16
2020A General Model of Author “Style” with Application to the UK House of Commons, 1935–2018Arthur Spirling16
2005Parties in elections, parties in government, and partisan biasAdam Meirowitz15
2002Isolating Spatial Autocorrelation, Aggregation Bias, and Distributional Violations in Ecological InferenceGary King13
2023The Essential Role of Statistical Inference in Evaluating Electoral Systems: A Response to DeFord et al.Gary King7
2025Measuring Distances in High Dimensional Spaces Why Average Group Vector Comparisons Exhibit Bias, And What to Do About itArthur Spirling7
2015Explaining Systematic Bias and Nontransparency in US Social Security Administration ForecastsGary King6
2002A Practical Statistical Model for Multiparty Electoral DataGary King6
2009A Comment on Diagnostic Tools for Counterfactual InferenceNicholas Sambanis2
2006Ancillary Materials in Volume 14Alan S. Gerber · Donald P. Green2
2009Empirical versus Theoretical Claims about Extreme Counterfactuals: A ResponseGary King1
2022Rejoinder: Concluding Remarks on Scholarly CommunicationsGary King0
2009The Society for Political MethodologyGary King
2018Comments on Single-Blind Reviewing from the Editorial StaffDaniel J. Hopkins
2002Experimental Methodology in Political ScienceROSE McDERMOTT

Citations as counted, before any journal weight: 76 of these 79 works carry a count, and a work with no count is unknown, never a zero.