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

Mean citations per academic
1 Princeton English 25 academics 21
2 Columbia English 34 academics 8
3 Cornell English 17 academics 2
4 Harvard English 18 academics 1
5 Penn English 22 academics 1
6 Dartmouth English 23 academics 0
7 Brown English 4 academics —
8 Yale English 13 academics —

870 citations over 20 of 54 works (37% carry a count). 13 of 156 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 Each point

Brown EnglishColumbia EnglishCornell EnglishDartmouth EnglishHarvard EnglishPenn EnglishPrinceton EnglishYale English

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
Yale English 13 2/13 0.15384615384615385% 0/13 0.0% 1/13 0.07692307692307693% 0 —
Brown English 4 0/4 0.0% 1/4 0.25% 0/4 0.0% 0 —
Columbia English 34 0/34 0.0% 2/34 0.058823529411764705% 0/34 0.0% 12 2/12 0.16666666666666666%
Princeton English 25 0/25 0.0% 3/25 0.12% 0/25 0.0% 6 5/6 0.8333333333333334%
Dartmouth English 23 0/23 0.0% 2/23 0.08695652173913043% 0/23 0.0% 2 1/2 0.5%
Penn English 22 0/22 0.0% 4/22 0.18181818181818182% 0/22 0.0% 14 6/14 0.42857142857142855%
Harvard English 18 0/18 0.0% 2/18 0.1111111111111111% 0/18 0.0% 5 3/5 0.6%
Cornell English 17 4/17 0.23529411764705882% 4/17 0.23529411764705882% 1/17 0.058823529411764705% 15 3/15 0.2%
  • 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 Each point

Brown EnglishColumbia EnglishCornell EnglishDartmouth EnglishHarvard EnglishPenn EnglishPrinceton EnglishYale English

Citations received in each year by the works these filters select.

What these filters select, per department not what the graph divided by
Department Academics CVScholarOpenAlex Works With a count
Yale English 13 2/13 0.15384615384615385% 0/13 0.0% 1/13 0.07692307692307693% 0 —
Brown English 4 0/4 0.0% 1/4 0.25% 0/4 0.0% 0 —
Columbia English 34 0/34 0.0% 2/34 0.058823529411764705% 0/34 0.0% 12 2/12 0.16666666666666666%
Princeton English 25 0/25 0.0% 3/25 0.12% 0/25 0.0% 6 5/6 0.8333333333333334%
Dartmouth English 23 0/23 0.0% 2/23 0.08695652173913043% 0/23 0.0% 2 1/2 0.5%
Penn English 22 0/22 0.0% 4/22 0.18181818181818182% 0/22 0.0% 14 6/14 0.42857142857142855%
Harvard English 18 0/18 0.0% 2/18 0.1111111111111111% 0/18 0.0% 5 3/5 0.6%
Cornell English 17 4/17 0.23529411764705882% 4/17 0.23529411764705882% 1/17 0.058823529411764705% 15 3/15 0.2%
  • 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: Other.010203040Brown Other 44Columbia Other 3434Cornell Other 1717Dartmouth Other 2323Harvard Other 1818Penn Other 2222Princeton Other 2525Yale Other 1313BrownColumbiaCornellDartmouthHarvardPennPrincetonYaleAcademicsDepartment

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
Yale English 13 2/13 0.15384615384615385% 0/13 0.0% 1/13 0.07692307692307693% 0 —
Brown English 4 0/4 0.0% 1/4 0.25% 0/4 0.0% 0 —
Columbia English 34 0/34 0.0% 2/34 0.058823529411764705% 0/34 0.0% 12 2/12 0.16666666666666666%
Princeton English 25 0/25 0.0% 3/25 0.12% 0/25 0.0% 6 5/6 0.8333333333333334%
Dartmouth English 23 0/23 0.0% 2/23 0.08695652173913043% 0/23 0.0% 2 1/2 0.5%
Penn English 22 0/22 0.0% 4/22 0.18181818181818182% 0/22 0.0% 14 6/14 0.42857142857142855%
Harvard English 18 0/18 0.0% 2/18 0.1111111111111111% 0/18 0.0% 5 3/5 0.6%
Cornell English 17 4/17 0.23529411764705882% 4/17 0.23529411764705882% 1/17 0.058823529411764705% 15 3/15 0.2%
  • 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

Median citations per academic at each rank
Value Measure

Nothing to draw under these filters.

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
Yale English 13 2/13 0.15384615384615385% 0/13 0.0% 1/13 0.07692307692307693% 0 —
Brown English 4 0/4 0.0% 1/4 0.25% 0/4 0.0% 0 —
Columbia English 34 0/34 0.0% 2/34 0.058823529411764705% 0/34 0.0% 12 2/12 0.16666666666666666%
Princeton English 25 0/25 0.0% 3/25 0.12% 0/25 0.0% 6 5/6 0.8333333333333334%
Dartmouth English 23 0/23 0.0% 2/23 0.08695652173913043% 0/23 0.0% 2 1/2 0.5%
Penn English 22 0/22 0.0% 4/22 0.18181818181818182% 0/22 0.0% 14 6/14 0.42857142857142855%
Harvard English 18 0/18 0.0% 2/18 0.1111111111111111% 0/18 0.0% 5 3/5 0.6%
Cornell English 17 4/17 0.23529411764705882% 4/17 0.23529411764705882% 1/17 0.058823529411764705% 15 3/15 0.2%
  • 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: Brown English, Columbia English, Cornell English, Dartmouth English, Harvard English, Penn English, Princeton English, Yale English.01002003004005006001 Brown English Aliyyah Abdur-Rahman 001 Columbia English Joseph R. Slaughter 1781781 Cornell English Rebeca Hey-Colón 30301 Dartmouth English Carolyn Dever 991 Harvard English Christopher Pexa 12121 Penn English Julia Alekseyeva 10101 Princeton English Rob Nixon 5035031 Yale English Ardis Butterfield 002 Brown English Dixa Ramirez 002 Columbia English Jenny Davidson 96962 Cornell English Lenora Warren 442 Dartmouth English Alexander Chee 002 Harvard English Beth Blum 442 Penn English Sara Kazmi 332 Princeton English Zahid R. Chaudhary 13132 Yale English Elleza Kelley 003 Brown English Mariah Min 003 Columbia English Alan Stewart 003 Cornell English Adhy Kim 003 Dartmouth English Alysia Garrison 003 Harvard English Amanda Claybaugh 003 Penn English Jean-Christophe Cloutier 113 Princeton English Robert Spoo 773 Yale English Ernest Mitchell 004 Brown English Rebecca Liu 004 Columbia English Amy E. Hungerford 004 Cornell English Alexandra Kleeman 004 Dartmouth English Anjuli F Kolb 004 Harvard English Anna Wilson 004 Penn English Abdulhamit Arvas 004 Princeton English Andrew Cole 004 Yale English Feisal Mohamed 005 Columbia English Andrew Delbanco 005 Cornell English Andrew Galloway 005 Dartmouth English Barbara Will 005 Harvard English Deidre Shauna Lynch 005 Penn English Caz Batten 005 Princeton English Anne A. Cheng 005 Yale English Jacqueline Goldsby 0012345CitationsRank

Brown EnglishColumbia EnglishCornell EnglishDartmouth EnglishHarvard EnglishPenn EnglishPrinceton EnglishYale English

Rank 5 of 34 at the deepest; 7 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
Abdulhamit Arvas 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Adhy Kim 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Alan Stewart 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Alexander Chee 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Alexandra Kleeman 1 1/1 1.0% 0/1 0.0% 0/1 0.0% 0 —
Aliyyah Abdur-Rahman 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Alysia Garrison 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Amanda Claybaugh 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Amy E. Hungerford 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Andrew Cole 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Andrew Delbanco 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Andrew Galloway 1 1/1 1.0% 0/1 0.0% 0/1 0.0% 0 —
Anjuli F Kolb 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Anna Shechtman 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Anna Wilson 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Anne A. Cheng 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Ardis Butterfield 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Austin E. Quigley 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Autumn Womack 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Barbara Will 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Bradin Cormack 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Branka Arsić 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Brent Hayes Edwards 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
C Riley Snorton 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Caroline Levine 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Caz Batten 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Chelsea Mikael Frazier 1 0/1 0.0% 1/1 1.0% 0/1 0.0% 0 —
Chi-ming Yang 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Colleen Glenney Boggs 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Colm Tóibín 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
D. Vance Smith 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
David Kazanjian 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
David L. Eng 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
David Wallace 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
David Yerkes 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Deidre Shauna Lynch 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Dixa Ramirez 1 0/1 0.0% 1/1 1.0% 0/1 0.0% 0 —
Dustin D. Stewart 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Eduardo Cadava 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Edward Mendelson 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Eleanor B Johnson 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Elleza Kelley 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
Ellis Hanson 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0 —
Emily Fridlund 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Emily Steiner 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Emily Steinlight 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Erik Gray 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Ernest Mitchell 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Esther Schor 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Ethan A. Plaue 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Farah Jasmine Griffin 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Feisal Mohamed 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Frances Negron-Muntaner 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Gauri Viswanathan 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Gayatri Chakravorty Spivak 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Gayle Salamon 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Gene Andrew Jarrett 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
George Edmondson 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Glenda Carpio 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Gordon Teskey 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Henry Louis Gates, Jr. 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Homi Bhabha 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Jack Halberstam 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Jacqueline Goldsby 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
James Adams 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Jean-Michel Rabaté 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Jed Esty 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Jeff Nunokawa 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Jeff Sharlet 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Jennifer Ponce de León 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Jesse McCarthy 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Jessica C. Beckman 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Jessica M. Rosenberg 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Jill Campbell 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Jodi Kim 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Joe Cleary 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Joseph Albernaz 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Joseph North 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Josephine Park 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Joshua Kotin 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Ju Yon Kim 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Juliana Hu Pegues 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Julie Crawford 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Kathy Eden 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Kimberly Juanita Brown 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Kinohi Nishikawa 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Langdon Hammer 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Laura K. Nelson 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Lauren Robertson 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Lee Clark Mitchell 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Lilith Todd 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Lindsay Thomas 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Louis Menand 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Margo Natalie Crawford 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Maria A. DiBattista 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Mariah Min 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Matthew Hart 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Matthew S. Olzmann 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Melanie B. Taylor 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Melissa F. Zeiger 1 0/1 0.0% 1/1 1.0% 0/1 0.0% 0 —
Meredith Martin 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Michael A. Chaney 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Michael Gamer 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Molly Murray 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Namwali Serpell 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Nancy Bentley 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Nancy Yousef 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Naomi Levine 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Nicholas Watson 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Nicole Morris Johnson 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Nicole Sheriko 1 1/1 1.0% 0/1 0.0% 0/1 0.0% 0 —
Nirvana Tanoukhi 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
NoViolet Bulawayo 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Patricia Dailey 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Patricia Rachael Stuelke 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Peter M. Orner 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Rebecca Liu 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Robert O'Meally 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Russ Leo 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Saidiya V Hartman 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Sam Moodie 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Sandeep Parmar 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Sandhya E. Dirks 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Sarah Cole 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Sarah Rivett 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Shana L. Redmond 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Shirley Samuels 1 1/1 1.0% 0/1 0.0% 0/1 0.0% 0 —
Simone White 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Sophie Gee 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Stefanie Markovits 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Stephanie Burt 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Stephen Greenblatt 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Susan Wolfson 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
T. Austin Graham 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Tamsen Wolff 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Thomas O'Malley 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Tracy K. Smith 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Vidyan Ravinthiran 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Vievee Elaure Francis 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
William A. Gleason 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Zita Cristina Nunes 1 0/1 0.0% 0/1 0.0% 0/1 0.0% 0 —
Dagmawi Woubshet 1 0/1 0.0% 1/1 1.0% 0/1 0.0% 3 0/3 0.0%
Mary Loeffelholz 1 0/1 0.0% 1/1 1.0% 0/1 0.0% 4 0/4 0.0%
Beth Blum 1 0/1 0.0% 1/1 1.0% 0/1 0.0% 1 1/1 1.0%
Carolyn Dever 1 0/1 0.0% 1/1 1.0% 0/1 0.0% 2 1/2 0.5%
Christopher Pexa 1 0/1 0.0% 1/1 1.0% 0/1 0.0% 4 2/4 0.5%
Jean-Christophe Cloutier 1 0/1 0.0% 1/1 1.0% 0/1 0.0% 2 1/2 0.5%
Jenny Davidson 1 0/1 0.0% 1/1 1.0% 0/1 0.0% 9 1/9 0.1111111111111111%
Joseph R. Slaughter 1 0/1 0.0% 1/1 1.0% 0/1 0.0% 3 1/3 0.3333333333333333%
Julia Alekseyeva 1 0/1 0.0% 1/1 1.0% 0/1 0.0% 6 3/6 0.5%
Lenora Warren 1 0/1 0.0% 1/1 1.0% 0/1 0.0% 5 1/5 0.2%
Rebeca Hey-Colón 1 0/1 0.0% 1/1 1.0% 0/1 0.0% 6 2/6 0.3333333333333333%
Rob Nixon 1 0/1 0.0% 1/1 1.0% 0/1 0.0% 1 1/1 1.0%
Robert Spoo 1 0/1 0.0% 1/1 1.0% 0/1 0.0% 3 3/3 1.0%
Sara Kazmi 1 0/1 0.0% 1/1 1.0% 0/1 0.0% 3 2/3 0.6666666666666666%
Zahid R. Chaudhary 1 0/1 0.0% 1/1 1.0% 0/1 0.0% 2 1/2 0.5%
  • 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.Value in HealthValue in Health 55LeviathanLeviathan 44Human Rights QuarterlyHuman Rights Quarterly 22American Literary HistoryAmerican Literary History 22History of the PresentHistory of the Present 11Studies in the novelStudies in the novel 11Radical History ReviewRadical History Review 11RePEc: Research Papers in EconomicsRePEc: Research Papers in Economics 11Aztlán A Journal of Chicano StudiesAztlán A Journal of Chicano Studies 11James Joyce quarterlyJames Joyce quarterly 11Black CameraBlack Camera 11CallalooCallaloo 11differencesdifferences 11Endocrine AbstractsEndocrine Abstracts 11Modern PhilologyModern Philology 11012345JournalWorks

The twelve venues highest on this reading, of 19.

What these filters select, per department not what the graph divided by
Department Academics CVScholarOpenAlex Works With a count
Yale English 13 2/13 0.15384615384615385% 0/13 0.0% 1/13 0.07692307692307693% 0 —
Brown English 4 0/4 0.0% 1/4 0.25% 0/4 0.0% 0 —
Columbia English 34 0/34 0.0% 2/34 0.058823529411764705% 0/34 0.0% 12 2/12 0.16666666666666666%
Princeton English 25 0/25 0.0% 3/25 0.12% 0/25 0.0% 6 5/6 0.8333333333333334%
Dartmouth English 23 0/23 0.0% 2/23 0.08695652173913043% 0/23 0.0% 2 1/2 0.5%
Penn English 22 0/22 0.0% 4/22 0.18181818181818182% 0/22 0.0% 14 6/14 0.42857142857142855%
Harvard English 18 0/18 0.0% 2/18 0.1111111111111111% 0/18 0.0% 5 3/5 0.6%
Cornell English 17 4/17 0.23529411764705882% 4/17 0.23529411764705882% 1/17 0.058823529411764705% 15 3/15 0.2%
  • 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.