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

Departmentshere

  • 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

Journals

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.
Trackademia
Value Measure

Departments

Citations by mean * log(1 + people)
1 Brown Political Science 27 academics 1,229.5
2 Princeton Politics 47 academics 1,209.7
3 Dartmouth Government 28 academics 1,185.7
4 Harvard Government 34 academics 1,168.6
5 Columbia Political Science 35 academics 1,150.6
6 Cornell Government 31 academics 842.5
7 Yale Political Science 34 academics 821.2
8 Penn Political Science 33 academics 803.8

68,382 citations over 1,819 of 2,493 works (73% carry a count). 241 of 269 academics have at least one counted work. A work with no citation count is unknown here, never a zero.

Publications over time

Works published in each year
Measure X axis

Dartmouth GovernmentHarvard GovernmentYale Political SciencePrinceton PoliticsColumbia Political ScienceCornell GovernmentPenn Political ScienceBrown Political Science

Every selected work, by the year it was published. An undated work is in no year.

What these filters select, per department not what the graph divided by
Department Academics CVScholarOpenAlex Works With a count
Princeton Politics 47 47/47 1.0% 38/47 0.8085106382978723% 45/47 0.9574468085106383% 340 280/340 0.8235294117647058%
Columbia Political Science 35 35/35 1.0% 24/35 0.6857142857142857% 35/35 1.0% 418 247/418 0.5909090909090909%
Harvard Government 34 34/34 1.0% 26/34 0.7647058823529411% 33/34 0.9705882352941176% 368 282/368 0.7663043478260869%
Yale Political Science 34 34/34 1.0% 32/34 0.9411764705882353% 33/34 0.9705882352941176% 260 220/260 0.8461538461538461%
Penn Political Science 33 33/33 1.0% 28/33 0.8484848484848485% 33/33 1.0% 311 206/311 0.662379421221865%
Cornell Government 31 31/31 1.0% 26/31 0.8387096774193549% 30/31 0.967741935483871% 368 264/368 0.717391304347826%
Dartmouth Government 28 28/28 1.0% 24/28 0.8571428571428571% 26/28 0.9285714285714286% 210 164/210 0.780952380952381%
Brown Political Science 27 27/27 1.0% 21/27 0.7777777777777778% 27/27 1.0% 218 156/218 0.7155963302752294%
  • Over the academics these filters select, so a filtered page describes the academics it selected and not the whole department.
  • A row with nothing held draws no mark above: the graph is quiet there because the evidence is missing, not because the work is.
  • What there was to draw from, not what the graph divided by - the graph states its own denominator above.
  • A Scholar profile is attached when at least 3 of the CV titles are found on it, or 30% of them are; one at another institution or under a different name needs 10 titles, and a profile far larger than the CV that holds only a small part of it is refused. Its articles are then checked one by one: what is not a work, or not by this person, is not counted.

Citations over time

Citations received in each year
Value Measure

Dartmouth GovernmentHarvard GovernmentYale Political SciencePrinceton PoliticsColumbia Political ScienceCornell GovernmentPenn Political ScienceBrown Political Science

Citations received in each year, from the academics’ verified Scholar profiles.

What these filters select, per department not what the graph divided by
Department Academics CVScholarOpenAlex Works With a count
Princeton Politics 47 47/47 1.0% 38/47 0.8085106382978723% 45/47 0.9574468085106383% 340 280/340 0.8235294117647058%
Columbia Political Science 35 35/35 1.0% 24/35 0.6857142857142857% 35/35 1.0% 418 247/418 0.5909090909090909%
Harvard Government 34 34/34 1.0% 26/34 0.7647058823529411% 33/34 0.9705882352941176% 368 282/368 0.7663043478260869%
Yale Political Science 34 34/34 1.0% 32/34 0.9411764705882353% 33/34 0.9705882352941176% 260 220/260 0.8461538461538461%
Penn Political Science 33 33/33 1.0% 28/33 0.8484848484848485% 33/33 1.0% 311 206/311 0.662379421221865%
Cornell Government 31 31/31 1.0% 26/31 0.8387096774193549% 30/31 0.967741935483871% 368 264/368 0.717391304347826%
Dartmouth Government 28 28/28 1.0% 24/28 0.8571428571428571% 26/28 0.9285714285714286% 210 164/210 0.780952380952381%
Brown Political Science 27 27/27 1.0% 21/27 0.7777777777777778% 27/27 1.0% 218 156/218 0.7155963302752294%
  • Over the academics these filters select, so a filtered page describes the academics it selected and not the whole department.
  • A row with nothing held draws no mark above: the graph is quiet there because the evidence is missing, not because the work is.
  • What there was to draw from, not what the graph divided by - the graph states its own denominator above.
  • A Scholar profile is attached when at least 3 of the CV titles are found on it, or 30% of them are; one at another institution or under a different name needs 10 titles, and a profile far larger than the CV that holds only a small part of it is refused. Its articles are then checked one by one: what is not a work, or not by this person, is not counted.

Makeup by tenure track

Academics on and off the tenure track
Series: Tenure track.01020304050Dartmouth Tenure track 2828Harvard Tenure track 3434Yale Tenure track 3434Princeton Tenure track 4747Columbia Tenure track 3535Cornell Tenure track 3131Penn Tenure track 3333Brown Tenure track 2727DartmouthHarvardYalePrincetonColumbiaCornellPennBrownAcademicsDepartment

Every academic the filters keep, by whether their current rank is on the tenure track (docs/definitions.md section 7). A rank in neither group - emeritus, postdoc, or a rank no CV states - is its own segment rather than filed under one of them: an emeritus professor was on the tenure track, and an unknown rank is not a claim about tenure at all.

What these filters select, per department not what the graph divided by
Department Academics CVScholarOpenAlex Works With a count
Princeton Politics 47 47/47 1.0% 38/47 0.8085106382978723% 45/47 0.9574468085106383% 340 280/340 0.8235294117647058%
Columbia Political Science 35 35/35 1.0% 24/35 0.6857142857142857% 35/35 1.0% 418 247/418 0.5909090909090909%
Harvard Government 34 34/34 1.0% 26/34 0.7647058823529411% 33/34 0.9705882352941176% 368 282/368 0.7663043478260869%
Yale Political Science 34 34/34 1.0% 32/34 0.9411764705882353% 33/34 0.9705882352941176% 260 220/260 0.8461538461538461%
Penn Political Science 33 33/33 1.0% 28/33 0.8484848484848485% 33/33 1.0% 311 206/311 0.662379421221865%
Cornell Government 31 31/31 1.0% 26/31 0.8387096774193549% 30/31 0.967741935483871% 368 264/368 0.717391304347826%
Dartmouth Government 28 28/28 1.0% 24/28 0.8571428571428571% 26/28 0.9285714285714286% 210 164/210 0.780952380952381%
Brown Political Science 27 27/27 1.0% 21/27 0.7777777777777778% 27/27 1.0% 218 156/218 0.7155963302752294%
  • Over the academics these filters select, so a filtered page describes the academics it selected and not the whole department.
  • A row with nothing held draws no mark above: the graph is quiet there because the evidence is missing, not because the work is.
  • What there was to draw from, not what the graph divided by - the graph states its own denominator above.
  • A Scholar profile is attached when at least 3 of the CV titles are found on it, or 30% of them are; one at another institution or under a different name needs 10 titles, and a profile far larger than the CV that holds only a small part of it is refused. Its articles are then checked one by one: what is not a work, or not by this person, is not counted.

Output by rank

Citations by mean * log(1 + people) at each rank
Value Measure
Series: Dartmouth Government, Harvard Government, Yale Political Science, Princeton Politics, Columbia Political Science, Cornell Government, Penn Political Science, Brown Political Science.0.0500.01,000.01,500.02,000.0Distinguished Dartmouth Government over 7 academics 1,701.6Distinguished Harvard Government over 24 academics 883.6Distinguished Yale Political Science over 11 academics 427.9Distinguished Princeton Politics over 13 academics 464.9Distinguished Columbia Political Science over 12 academics 917.4Distinguished Cornell Government over 6 academics 789.1Distinguished Penn Political Science over 15 academics 841.9Distinguished Brown Political Science over 12 academics 1,513.3Full Dartmouth Government over 9 academics 190.9Full Harvard Government over 3 academics 1,249.1Full Yale Political Science over 5 academics 730.7Full Princeton Politics over 15 academics 767.1Full Columbia Political Science over 10 academics 148.9Full Cornell Government over 7 academics 857.3Full Penn Political Science over 5 academics 313.2Full Brown Political Science over 3 academics 41.1Associate Dartmouth Government over 9 academics 603.8Associate Harvard Government over 1 academics 93.6Associate Yale Political Science over 7 academics 613.4Associate Princeton Politics over 6 academics 1,364.7Associate Columbia Political Science over 5 academics 445.4Associate Cornell Government over 10 academics 262.6Associate Penn Political Science over 6 academics 238.4Associate Brown Political Science over 7 academics 506.8Assistant Dartmouth Government over 3 academics 148.3Assistant Harvard Government over 6 academics 247.5Assistant Yale Political Science over 11 academics 314.7Assistant Princeton Politics over 13 academics 502.6Assistant Columbia Political Science over 8 academics 513.9Assistant Cornell Government over 8 academics 174.7Assistant Penn Political Science over 7 academics 200.2Assistant Brown Political Science over 5 academics 257.3DistinguishedFullAssociateAssistantCitations by mean * log(1 + people)Rank

Dartmouth GovernmentHarvard GovernmentYale Political SciencePrinceton PoliticsColumbia Political ScienceCornell GovernmentPenn Political ScienceBrown Political Science

Each rank read on its own, so a department of thirty and one of sixty are comparable at the same career stage.

What these filters select, per department not what the graph divided by
Department Academics CVScholarOpenAlex Works With a count
Princeton Politics 47 47/47 1.0% 38/47 0.8085106382978723% 45/47 0.9574468085106383% 340 280/340 0.8235294117647058%
Columbia Political Science 35 35/35 1.0% 24/35 0.6857142857142857% 35/35 1.0% 418 247/418 0.5909090909090909%
Harvard Government 34 34/34 1.0% 26/34 0.7647058823529411% 33/34 0.9705882352941176% 368 282/368 0.7663043478260869%
Yale Political Science 34 34/34 1.0% 32/34 0.9411764705882353% 33/34 0.9705882352941176% 260 220/260 0.8461538461538461%
Penn Political Science 33 33/33 1.0% 28/33 0.8484848484848485% 33/33 1.0% 311 206/311 0.662379421221865%
Cornell Government 31 31/31 1.0% 26/31 0.8387096774193549% 30/31 0.967741935483871% 368 264/368 0.717391304347826%
Dartmouth Government 28 28/28 1.0% 24/28 0.8571428571428571% 26/28 0.9285714285714286% 210 164/210 0.780952380952381%
Brown Political Science 27 27/27 1.0% 21/27 0.7777777777777778% 27/27 1.0% 218 156/218 0.7155963302752294%
  • Over the academics these filters select, so a filtered page describes the academics it selected and not the whole department.
  • A row with nothing held draws no mark above: the graph is quiet there because the evidence is missing, not because the work is.
  • What there was to draw from, not what the graph divided by - the graph states its own denominator above.
  • A Scholar profile is attached when at least 3 of the CV titles are found on it, or 30% of them are; one at another institution or under a different name needs 10 titles, and a profile far larger than the CV that holds only a small part of it is refused. Its articles are then checked one by one: what is not a work, or not by this person, is not counted.

Academics ranked against academics

Each department's rank-n academic, citations
Value Ranks
Series: Dartmouth Government, Harvard Government, Yale Political Science, Princeton Politics, Columbia Political Science, Cornell Government, Penn Political Science, Brown Political Science.010002000300040001 Dartmouth Government Brendan Nyhan 37871 Harvard Government Kosuke Imai 24761 Yale Political Science Joshua Kalla 15311 Princeton Politics Andrew M. Guess 32711 Columbia Political Science Andrew Gelman 29501 Cornell Government Jamila Michener 11621 Penn Political Science Matthew Levendusky 10541 Brown Political Science Eric M. Patashnik 34702 Dartmouth Government Sean J. Westwood 13352 Harvard Government Joshua David Kertzer 13022 Yale Political Science Daniel C. Mattingly 7132 Princeton Politics Rocío Titiunik 26672 Columbia Political Science Donald P. Green 6892 Cornell Government Peter John Loewen 9752 Penn Political Science Guy Grossman 9632 Brown Political Science Peter Andreas 25773 Dartmouth Government Jennifer Jerit 10303 Harvard Government Dustin Tingley 10843 Yale Political Science Gregory Alain Huber 4633 Princeton Politics Arthur Spirling 10153 Columbia Political Science John Marshall 6703 Cornell Government Sarah E. Kreps 8913 Penn Political Science Michael Horowitz 8923 Brown Political Science Robert A. Blair 10324 Dartmouth Government Jeremy Ferwerda 5534 Harvard Government Steven Levitsky 6184 Yale Political Science Shiro Kuriwaki 4604 Princeton Politics Christopher Blair 6254 Columbia Political Science Yamil Ricardo Velez 6064 Cornell Government Thomas B. Pepinsky 6614 Penn Political Science Daniel J. Hopkins 7234 Brown Political Science Mark McGann Blyth 4145 Dartmouth Government John Michael Carey 5255 Harvard Government Gary King 6125 Yale Political Science P. M. Aronow 4525 Princeton Politics Guadalupe Tuñón 5175 Columbia Political Science Nikhar Gaikwad 4665 Cornell Government Douglas L. Kriner 4855 Penn Political Science Sarah Bush 4925 Brown Political Science Donghyun Danny Choi 39612345CitationsRank

Dartmouth GovernmentHarvard GovernmentYale Political SciencePrinceton PoliticsColumbia Political ScienceCornell GovernmentPenn Political ScienceBrown Political Science

Rank 5 of 47 at the deepest; 8 of 8 departments still have somebody at the last rank drawn. Rank n of one department is not otherwise related to rank n of another - what this compares is the shape of the lists.

What these filters select, per academic not what the graph divided by
Academic Academics CVScholarOpenAlex Works With a count
Alex Weisiger 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
Andrew Moravcsik 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
Claudine Gay 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
Daniel Gillion 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
David C. Johnston 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
Dean Lacy 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
Fredrick C. Harris 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
G. John Ikenberry 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
Ira I. Katznelson 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
Jason Frank 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
Jean Louise Cohen 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
Jeffrey R. Lax 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
Jennifer A. Widner 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
Michael J. Hiscox 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
Michael M. Ting 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
Robert Y. Shapiro 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
Shigeo Hirano 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
Sonu Bedi 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 0 —
Stephen Daniel Ansolabehere 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
Stephen Skowronek 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
Temi Ogunye 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 0 —
Allen R. Carlson 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 1 0/1 0.0%
Blake Miller 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 2 0/2 0.0%
Edward S. Steinfeld 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 1 0/1 0.0%
Jack L. Snyder 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 1 0/1 0.0%
Melissa Sharon Lane 1 1/1 1.0% 0/1 0.0% 0/1 0.0% 4 0/4 0.0%
Sharyn O’Halloran 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 1 0/1 0.0%
Steven I. Wilkinson 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 2 0/2 0.0%
Adam Meirowitz 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 4 4/4 1.0%
Adam Michael Auerbach 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 5/5 1.0%
Aditi Sahasrabuddhe 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 10 7/10 0.7%
Ainsley Lesure 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 1 1/1 1.0%
Akshay Govind Dixit 1 1/1 1.0% 1/1 1.0% 0/1 0.0% 3 2/3 0.6666666666666666%
Alan S. Gerber 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 17 14/17 0.8235294117647058%
Alessandra M. Casella 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 4/5 0.8%
Alexander Hirschman Gourevitch 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 4 2/4 0.5%
Alexander Livingston 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 2/8 0.25%
Alexandre Debs 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 3 3/3 1.0%
Alisha C. Holland 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 9 6/9 0.6666666666666666%
Allison Carnegie 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 20 13/20 0.65%
Allison P. Harris 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 4 4/4 1.0%
Amanda Weiss 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 3 3/3 1.0%
Amaney A. Jamal 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 12 10/12 0.8333333333333334%
Amy Gutmann 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 4 1/4 0.25%
Ana De La O Torres 1 1/1 1.0% 1/1 1.0% 0/1 0.0% 6 6/6 1.0%
Andreas Wiedemann 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 10 10/10 1.0%
Andreas Wimmer 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 14 8/14 0.5714285714285714%
Andrew Gelman 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 76 67/76 0.881578947368421%
Andrew J. McCall 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 1 1/1 1.0%
Andrew J. Nathan 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 110 13/110 0.11818181818181818%
Andrew M. Guess 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 20 19/20 0.95%
Arthur Spirling 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 11 10/11 0.9090909090909091%
Ashutosh Varshney 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 2 2/2 1.0%
Atul Kohli 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 4 3/4 0.75%
Averell Schmidt 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 3 3/3 1.0%
Benjamin A. Valentino 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 6 6/6 1.0%
Beth Simmons 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 14 11/14 0.7857142857142857%
Brendan Nyhan 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 40 38/40 0.95%
Brendan O'Leary 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 49 5/49 0.10204081632653061%
Bryan Garsten 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 2/5 0.4%
Bryn Rosenfeld 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 5/8 0.625%
Calvin Thrall 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 5/5 1.0%
Carles Boix 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 8/8 1.0%
Carlo Prato 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 7/8 0.875%
Charles M. Cameron 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 6 6/6 1.0%
Charles T. Mcclean 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 17 14/17 0.8235294117647058%
Christian "Chris" Chambers 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 2 1/2 0.5%
Christina L. Davis 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 7 6/7 0.8571428571428571%
Christina M. Kinane 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 2 1/2 0.5%
Christoph Mikulaschek 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 1 1/1 1.0%
Christopher Blair 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 23 15/23 0.6521739130434783%
Corey Brettschneider 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 3/5 0.6%
Daniel C. Mattingly 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 10 9/10 0.9%
Daniel J. Hopkins 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 20 20/20 1.0%
Daniel M. Smith 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 13 10/13 0.7692307692307693%
Daniel N.j. de Kadt 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 3 3/3 1.0%
Daniel Paul Carpenter 1 1/1 1.0% 1/1 1.0% 0/1 0.0% 17 11/17 0.6470588235294118%
Daniel Ziblatt 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 20 11/20 0.55%
Danielle S. Allen 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 11 8/11 0.7272727272727273%
Daryl G. Press 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 10 7/10 0.7%
David Alexander Bateman 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 5/5 1.0%
David Skarbek 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 4 4/4 1.0%
Deborah J. Yashar 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 3 1/3 0.3333333333333333%
Deborah Jordan Brooks 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 1 1/1 1.0%
Diana C. Mutz 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 7 4/7 0.5714285714285714%
Diana Fu 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 6 6/6 1.0%
Didac Queralt 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 5/5 1.0%
Dillon Laaker 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 1 1/1 1.0%
Donald P. Green 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 34 29/34 0.8529411764705882%
Donghyun Danny Choi 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 11 10/11 0.9090909090909091%
Douglas L. Kriner 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 19 14/19 0.7368421052631579%
Dustin Tingley 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 14 12/14 0.8571428571428571%
Edward D. Mansfield 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 9 8/9 0.8888888888888888%
Egor Lazarev 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 2/5 0.4%
Elisabeth Jean Wood 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 6/8 0.75%
Elizabeth J. Perry 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 4/5 0.8%
Elizabeth Nathan Saunders 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 9 3/9 0.3333333333333333%
Elizabeth Parker-Magyar 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 6 2/6 0.3333333333333333%
Elizabeth R. Nugent 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 7/8 0.875%
Emily A. Sellars 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 4 4/4 1.0%
Eric M. Patashnik 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 13 9/13 0.6923076923076923%
Eric Nelson 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 3 1/3 0.3333333333333333%
Erik Wibbels 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 11 10/11 0.9090909090909091%
Eunji Kim 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 9 8/9 0.8888888888888888%
Feyaad Allie 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 3 3/3 1.0%
Fiona S. Cunningham 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 4/5 0.8%
Frances E. Lee 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 6 6/6 1.0%
Gareth Nellis 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 5/5 1.0%
Gary King 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 38 31/38 0.8157894736842105%
Gemma Dipoppa 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 6/8 0.75%
GERARD PADRÓ i MIQUEL 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 9 8/9 0.8888888888888888%
Germán Gieczewski 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 8/8 1.0%
Gleason Judd 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 5/5 1.0%
Gregory Alain Huber 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 31 29/31 0.9354838709677419%
Gregory Conti 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 3 2/3 0.6666666666666666%
Gregory J. Wawro 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 3/5 0.6%
Grigore Pop-Eleches 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 13 12/13 0.9230769230769231%
Grzegorz Ekiert 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 2 2/2 1.0%
Guadalupe Tuñón 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 8/8 1.0%
Gustavo A. Flores-Macías 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 10 7/10 0.7%
Guy Grossman 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 15 15/15 1.0%
Helen V. Milner 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 14 11/14 0.7857142857142857%
Hye Young You 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 10 10/10 1.0%
Hélène Landemore 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 16 12/16 0.75%
Ian Shapiro 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 7 7/7 1.0%
Isabel María Perera 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 13 9/13 0.6923076923076923%
Ismail K. White 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 2 2/2 1.0%
Jacob S. Hacker 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 13 6/13 0.46153846153846156%
James Bernard Murphy 1 1/1 1.0% 1/1 1.0% 0/1 0.0% 5 2/5 0.4%
James M. Snyder, Jr. 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 8/8 1.0%
James Raymond Vreeland 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 11 8/11 0.7272727272727273%
Jamila Michener 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 40 31/40 0.775%
Jane Esberg 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 7 5/7 0.7142857142857143%
Jason Barabas 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 4 4/4 1.0%
Jason Lyall 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 5/5 1.0%
Jeff D. Colgan 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 19 8/19 0.42105263157894735%
Jeffrey A. Friedman 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 15 10/15 0.6666666666666666%
Jeffrey Edward Green 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 5 3/5 0.6%
Jennifer Gandhi 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 2 1/2 0.5%
Jennifer Hadden 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 16 13/16 0.8125%
Jennifer Hochschild 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 4/8 0.5%
Jennifer Jerit 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 4 4/4 1.0%
JENNIFER M. LIND 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 9 5/9 0.5555555555555556%
Jeremy Ferwerda 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 6 6/6 1.0%
John Benedict Londregan 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 1 1/1 1.0%
John D. Huber 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 1 1/1 1.0%
John Marshall 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 10 10/10 1.0%
John Michael Carey 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 12 10/12 0.8333333333333334%
Jonathan Mummolo 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 5/5 1.0%
Jonathan P. Kastellec 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 10 9/10 0.9%
Joshua David Kertzer 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 16 15/16 0.9375%
Joshua Kalla 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 16 16/16 1.0%
Julia Gray 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 6 4/6 0.6666666666666666%
Julia Lynch 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 12 8/12 0.6666666666666666%
Julie L. Rose 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 4 1/4 0.25%
Junyan Jiang 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 6/8 0.75%
Justin H. Phillips 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 2 2/2 1.0%
Kate Baldwin 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 10 9/10 0.9%
Katherine Irajpanah 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 1 1/1 1.0%
Katherine Tate 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 7 6/7 0.8571428571428571%
Kathleen E. Powers 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 7/8 0.875%
Keidrick J. Roy 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 9 6/9 0.6666666666666666%
Kenneth Roberts 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 16 10/16 0.625%
Kevin DeLuca 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 6 6/6 1.0%
Kosuke Imai 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 49 42/49 0.8571428571428571%
Kristopher W. Ramsay 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 6 4/6 0.6666666666666666%
Lafleur Stephens-Dougan 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 2 1/2 0.5%
Layna Mosley 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 14 6/14 0.42857142857142855%
Leonard Wantchekon 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 12 10/12 0.8333333333333334%
Lisa Baldez 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 3 1/3 0.3333333333333333%
Loren Goldman 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 10 8/10 0.8%
Luca Bellodi 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 3 3/3 1.0%
Lucas Swaine 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 2 2/2 1.0%
Marc Meredith 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 10 8/10 0.8%
Marcel F. Roman 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 11 11/11 1.0%
Margaret M. Weir 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 2/5 0.4%
Maria Victoria Murillo 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 13 8/13 0.6153846153846154%
Mark McGann Blyth 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 20 10/20 0.5%
Markus Prior 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 3 3/3 1.0%
Marques Zárate 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 4 4/4 1.0%
Mashail Malik 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 6 5/6 0.8333333333333334%
Matias Iaryczower 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 3 3/3 1.0%
Matthew Levendusky 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 18 14/18 0.7777777777777778%
Melani Cammett 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 6 6/6 1.0%
Melissa Lee 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 6 5/6 0.8333333333333334%
Melissa Lee Sands 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 7 6/7 0.8571428571428571%
Melissa Schwartzberg 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 7 5/7 0.7142857142857143%
Melvin L. Rogers 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 2 2/2 1.0%
Mia Costa 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 10 7/10 0.7%
Michael Eric ROSEN 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 5 4/5 0.8%
Michael Horowitz 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 40 22/40 0.55%
Michael Jones-Correa 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 4 3/4 0.75%
Michele F. Margolis 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 7 7/7 1.0%
Michelle T. Clarke 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 5/8 0.625%
Milan Svolik 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 2 2/2 1.0%
Nadia Urbinati 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 30 14/30 0.4666666666666667%
Naima Green-Riley 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 3 2/3 0.6666666666666666%
Nicholas Kuipers 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 12 11/12 0.9166666666666666%
Nicholas L. Miller 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 10 6/10 0.6%
Nicholas Sambanis 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 13 12/13 0.9230769230769231%
Nikhar Gaikwad 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 9 9/9 1.0%
Noah Zucker 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 6 5/6 0.8333333333333334%
Noam Reich 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 2 2/2 1.0%
Nolan McCarty 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 2/5 0.4%
Oumar Ba 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 16 10/16 0.625%
P. M. Aronow 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 15 13/15 0.8666666666666667%
Parrish Bergquist 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 11 11/11 1.0%
patchen markell 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 1 1/1 1.0%
Paul E. Peterson 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 36 19/36 0.5277777777777778%
Peter A. Hall 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 14 14/14 1.0%
Peter Andreas 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 3 2/3 0.6666666666666666%
Peter E. Buisseret 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 7 7/7 1.0%
Peter Joachim Katzenstein 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 9 2/9 0.2222222222222222%
Peter John Loewen 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 41 35/41 0.8536585365853658%
Peter K. Enns 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 16 11/16 0.6875%
Pia J. Raffler 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 4 4/4 1.0%
Prerna Singh 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 3 1/3 0.3333333333333333%
Rachel Beatty Riedl 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 11 9/11 0.8181818181818182%
Rafaela Dancygier 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 10 8/10 0.8%
Rebecca Weitz-Shapiro 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 8/8 1.0%
Reid B. C. Pauly 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 9 6/9 0.6666666666666666%
Richard F. Bensel 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 2/5 0.4%
Richard Owen Snyder 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 3 3/3 1.0%
Robert A. Blair 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 21 20/21 0.9523809523809523%
Rocío Titiunik 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 12 11/12 0.9166666666666666%
Rory Truex 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 9 5/9 0.5555555555555556%
ROSE McDERMOTT 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 20 10/20 0.5%
Roxanne L. Euben 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 1 1/1 1.0%
Rudra Sil 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 2 1/2 0.5%
Russell Muirhead 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 4 2/4 0.5%
Ryan D. Enos 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 9 4/9 0.4444444444444444%
Sabrina M. Karim 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 17 14/17 0.8235294117647058%
Sarah Bush 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 13 12/13 0.9230769230769231%
Sarah E. Kreps 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 44 29/44 0.6590909090909091%
Sarah F. Thompson 1 1/1 1.0% 1/1 1.0% 0/1 0.0% 1 1/1 1.0%
Sarah Zukerman Daly 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 12 8/12 0.6666666666666666%
Sean J. Westwood 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 25 23/25 0.92%
Sharon R. Krause 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 6 4/6 0.6666666666666666%
Shiro Kuriwaki 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 13 13/13 1.0%
Soosun You 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 3 2/3 0.6666666666666666%
Soyoung Lee 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 4 4/4 1.0%
Stephanie Ternullo 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 16 12/16 0.75%
Stephen Chaudoin 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 7 6/7 0.8571428571428571%
Stephen G. Brooks 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 9 7/9 0.7777777777777778%
Stephen Macedo 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 2 1/2 0.5%
Steven Levitsky 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 21 13/21 0.6190476190476191%
Susan L. Moffitt 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 5 5/5 1.0%
Suzanne Mettler 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 13 9/13 0.6923076923076923%
Taeku Lee 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 10 8/10 0.8%
Talbot M. Andrews 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 25 23/25 0.92%
Tali Mendelberg 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 18 16/18 0.8888888888888888%
Tanushree Goyal 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 5/5 1.0%
TARIQ THACHIL 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 4 3/4 0.75%
Theda Skocpol 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 2 2/2 1.0%
Thomas B. Pepinsky 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 36 24/36 0.6666666666666666%
Timothy M. Frye 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 10 7/10 0.7%
Torben Iversen 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 9 6/9 0.6666666666666666%
Tulia G. Falleti 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 3 2/3 0.6666666666666666%
Turkuler ISIKSEL 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 4 2/4 0.5%
Tyler Jost 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 13 13/13 1.0%
Virginia Page Fortna 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 3 3/3 1.0%
Wendy J. Schiller 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 3/5 0.6%
William C. Wohlforth 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 6 4/6 0.6666666666666666%
William R. Hobbs 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 9 7/9 0.7777777777777778%
Xu Xu 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 7 7/7 1.0%
Yamil Ricardo Velez 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 15 15/15 1.0%
Yang-Yang Zhou 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 8/8 1.0%
Yuhua Wang 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 11 9/11 0.8181818181818182%
Zeyang (Arthur) Yu 1 1/1 1.0% 1/1 1.0% 0/1 0.0% 4 4/4 1.0%
  • Over the academics these filters select, so a filtered page describes the academics it selected and not the whole department.
  • A row with nothing held draws no mark above: the graph is quiet there because the evidence is missing, not because the work is.
  • What there was to draw from, not what the graph divided by - the graph states its own denominator above.
  • A Scholar profile is attached when at least 3 of the CV titles are found on it, or 30% of them are; one at another institution or under a different name needs 10 titles, and a profile far larger than the CV that holds only a small part of it is refused. Its articles are then checked one by one: what is not a work, or not by this person, is not counted.

Years to promotion

From the PhD to each rank
Series: Dartmouth Government, Harvard Government, Yale Political Science, Princeton Politics, Columbia Political Science, Cornell Government, Penn Political Science, Brown Political Science.0.05.010.015.020.0Associate Dartmouth Government median of 2 academics 8.08.0Associate Harvard Government median of 1 academics 14.014.0Associate Yale Political Science median of 4 academics 9.09.0Associate Princeton Politics median of 6 academics 8.58.5Associate Columbia Political Science median of 3 academics 11.011.0Associate Cornell Government median of 5 academics 9.09.0Associate Penn Political Science median of 3 academics 9.09.0Associate Brown Political Science median of 2 academics 7.57.5Full or distinguished Dartmouth Government median of 2 academics 17.017.0Full or distinguished Harvard Government median of 2 academics 10.510.5Full or distinguished Yale Political Science median of 3 academics 12.012.0Full or distinguished Princeton Politics median of 3 academics 8.08.0Full or distinguished Columbia Political Science median of 2 academics 12.512.5Full or distinguished Cornell Government median of 2 academics 14.514.5Full or distinguished Penn Political Science median of 2 academics 14.014.0Full or distinguished Brown Political Science median of 3 academics 13.013.0AssociateFull or distinguishedYears since the PhDRank reached

Dartmouth GovernmentHarvard GovernmentYale Political SciencePrinceton PoliticsColumbia Political ScienceCornell GovernmentPenn Political ScienceBrown Political Science

The median years from the PhD year a CV states to the first year it states that rank. An academic whose CV dates neither is in neither figure.

What these filters select, per department not what the graph divided by
Department Academics CVScholarOpenAlex Works With a count
Princeton Politics 47 47/47 1.0% 38/47 0.8085106382978723% 45/47 0.9574468085106383% 340 280/340 0.8235294117647058%
Columbia Political Science 35 35/35 1.0% 24/35 0.6857142857142857% 35/35 1.0% 418 247/418 0.5909090909090909%
Harvard Government 34 34/34 1.0% 26/34 0.7647058823529411% 33/34 0.9705882352941176% 368 282/368 0.7663043478260869%
Yale Political Science 34 34/34 1.0% 32/34 0.9411764705882353% 33/34 0.9705882352941176% 260 220/260 0.8461538461538461%
Penn Political Science 33 33/33 1.0% 28/33 0.8484848484848485% 33/33 1.0% 311 206/311 0.662379421221865%
Cornell Government 31 31/31 1.0% 26/31 0.8387096774193549% 30/31 0.967741935483871% 368 264/368 0.717391304347826%
Dartmouth Government 28 28/28 1.0% 24/28 0.8571428571428571% 26/28 0.9285714285714286% 210 164/210 0.780952380952381%
Brown Political Science 27 27/27 1.0% 21/27 0.7777777777777778% 27/27 1.0% 218 156/218 0.7155963302752294%
  • Over the academics these filters select, so a filtered page describes the academics it selected and not the whole department.
  • A row with nothing held draws no mark above: the graph is quiet there because the evidence is missing, not because the work is.
  • What there was to draw from, not what the graph divided by - the graph states its own denominator above.
  • A Scholar profile is attached when at least 3 of the CV titles are found on it, or 30% of them are; one at another institution or under a different name needs 10 titles, and a profile far larger than the CV that holds only a small part of it is refused. Its articles are then checked one by one: what is not a work, or not by this person, is not counted.

Journals used

Where these departments place work, by works
Value
Series: value.Foreign AffairsForeign Affairs 150150The Journal of PoliticsThe Journal of Politics 104104American Journal of Political ScienceAmerican Journal of Political Science 9090American Political Science ReviewAmerican Political Science Review 7070Perspectives on PoliticsPerspectives on Politics 4646The Irish TimesThe Irish Times 4242Proceedings of the National Academy of SciencesProceedings of the National Academy of Sciences 4141British Journal of Political ScienceBritish Journal of Political Science 3434Comparative Political StudiesComparative Political Studies 3434International Studies QuarterlyInternational Studies Quarterly 3030Cambridge University Press eBooksCambridge University Press eBooks 2929International OrganizationInternational Organization 2929Journal of democracyJournal of democracy 2929Political Science Research and MethodsPolitical Science Research and Methods 2828World PoliticsWorld Politics 2727050100150JournalWorks

The twelve venues highest on this reading, of 498.

What these filters select, per department not what the graph divided by
Department Academics CVScholarOpenAlex Works With a count
Princeton Politics 47 47/47 1.0% 38/47 0.8085106382978723% 45/47 0.9574468085106383% 340 280/340 0.8235294117647058%
Columbia Political Science 35 35/35 1.0% 24/35 0.6857142857142857% 35/35 1.0% 418 247/418 0.5909090909090909%
Harvard Government 34 34/34 1.0% 26/34 0.7647058823529411% 33/34 0.9705882352941176% 368 282/368 0.7663043478260869%
Yale Political Science 34 34/34 1.0% 32/34 0.9411764705882353% 33/34 0.9705882352941176% 260 220/260 0.8461538461538461%
Penn Political Science 33 33/33 1.0% 28/33 0.8484848484848485% 33/33 1.0% 311 206/311 0.662379421221865%
Cornell Government 31 31/31 1.0% 26/31 0.8387096774193549% 30/31 0.967741935483871% 368 264/368 0.717391304347826%
Dartmouth Government 28 28/28 1.0% 24/28 0.8571428571428571% 26/28 0.9285714285714286% 210 164/210 0.780952380952381%
Brown Political Science 27 27/27 1.0% 21/27 0.7777777777777778% 27/27 1.0% 218 156/218 0.7155963302752294%
  • Over the academics these filters select, so a filtered page describes the academics it selected and not the whole department.
  • A row with nothing held draws no mark above: the graph is quiet there because the evidence is missing, not because the work is.
  • What there was to draw from, not what the graph divided by - the graph states its own denominator above.
  • A Scholar profile is attached when at least 3 of the CV titles are found on it, or 30% of them are; one at another institution or under a different name needs 10 titles, and a profile far larger than the CV that holds only a small part of it is refused. Its articles are then checked one by one: what is not a work, or not by this person, is not counted.