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Lucas Champollion – Associate professor of linguistics at NYU — 18 websites ranked by shared content topics, category and on-page relevance.

Each result shows its full tech stack, contacts and AI-policy — not just a name · Browse all sites in Education →

Domain	Match	Title	Country/Lang	Category	AI files	Contact	AI-protection
abcp.org.uk ↗	69 match 1 shared topics	ABCP – Association of British Chinese Professors	United Kingdom en	education WordPress	robotsllmsaihumans	emailphone	none
dhrice.org ↗	67 match 1 shared topics	Dan Hiaeshutter-Rice — Associate Professor, Michigan State University	United States~ en	education	robotsllmsaihumans	emailphone	none
langchallenge.org ↗	66 match 1 shared topics	International Linguistics Challenge	Germany~ en	education	robotsllmsaihumans	emailphone	none
mcgillscience.org ↗	66 match 1 shared topics	Association of McGill Professors of Science – Association of McGill Professors of Science	en	education WordPress WooCommerce	robotsllmsaihumans	emailphone	none
aleksandra-bartoszko.com ↗	66 match 1 shared topics	Aleksandra Bartoszko – Social anthropologist & professor of social work	en	education WordPress	robotsllmsaihumans	emailphone	none
bcnewell.com ↗	66 match 1 shared topics	Bryce Clayton Newell, PhD, JD – Petrone Fellow & Associate Professor \| School of Journalism and Communication \| University of Oregon	en	education WordPress	robotsllmsaihumans	emailphone	none
aaupbhsnj.org ↗	66 match 1 shared topics	AAUP BHSNJ – American Association of University Professors Biomedical and Health Sciences of New Jersey	en	education WordPress WooCommerce	robotsllmsaihumans	emailphone	none
alexanderkustov.org ↗	66 match 1 shared topics	Alexander Kustov \| Associate Professor of migration at the University of Notre Dame. Research on immigration, public opinion, and democratic policymaking.	en	education	robotsllmsaihumans	emailphone	none
michaelchen.co ↗	65 match 1 shared topics	Michael Chen, Ph.D. – Associate Dean of Student Success and Engagement	Colombia en	education WordPress WooCommerce	robotsllmsaihumans	emailphone	none
kristinlafollette.com ↗	65 match 1 shared topics	Kristin LaFollette, Ph.D. – Professor – Scholar – Writer	en	education WordPress	robotsllmsaihumans	emailphone	none
viviennewestbrook.com ↗	65 match 1 shared topics	Vivienne Ruth Westbrook, Ph.D. – Professor of English and Cultural Studies	en	education WordPress	robotsllmsaihumans	emailphone	none
csmowcub.org ↗	65 match 1 shared topics	Council of Statesmen – MOWCUB Alumni Association	en	education	robotsllmsaihumans	emailphone	none
korolova.com ↗	65 match 1 shared topics	Aleksandra Korolova, Assistant Professor of Computer Science and Public Affairs at Princeton	United States~ en	education	robotsllmsaihumans	emailphone	none
classicsmalta.org ↗	64 match 1 shared topics	Home - Malta Classics Association	en	education WordPress WooCommerce	robotsllmsaihumans	emailphone	none
ourbca.org ↗	64 match 1 shared topics	Brentwood Clerical Association – Brentwood Union Free School District	en	education WordPress	robotsllmsaihumans	emailphone	none
oxfordjunctionalumniassociation.com ↗	64 match 1 shared topics	OXFORD JUNCTION ALUMNI ASSOCIATION – "Where former Pirates hang out"	en	education WordPress	robotsllmsaihumans	emailphone	none
lamath.org ↗	64 match 1 shared topics	Louisiana Association of Teachers of Mathematics	en	education	robotsllmsaihumans	emailphone	none
koremassociates.com ↗	64 match 1 shared topics	Home - Korem & Associates	en	education WordPress	robots llmsaihumans	email phone	none

How the match score works

Each match is a 0–100 similarity score — the higher it is, the more two sites resemble one another. It’s computed automatically from our own crawl data (never from what a site says about itself) by combining several independent signals, so a high score means several of them point the same way:

shared topics how many of the same fine‑grained content topics (IAB sub‑categories) both sites cover — the main signal.
same category whether they sit in the same business / industry category.
content & title how closely their titles and page descriptions line up in wording and meaning.

No single signal decides the result — they’re blended together. Treat the score as a way to rank candidates rather than an absolute percentage; the chips on each result show which signals contributed.