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Jonathan S. Coley - Associate Professor of Sociology, Oklahoma State University — 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
joseejohnston.com ↗	71 match 1 shared topics	Josée Johnston – Professor of Sociology, University of Toronto	en	education WordPress	robotsllmsaihumans	emailphone	none
nathanfavero.com ↗	71 match 1 shared topics	Nathan Favero – Provost Associate Professor at American University	en	education WordPress	robotsllmsaihumans	emailphone	none
nasliionllc.com ↗	70 match 1 shared topics	Sharon A. Simmons, Ph.D. – Associate Professor of Entrepreneurship · Jackson State University \| JSU	US~ en	education WordPress	robotsllmsaihumans	email phone	none
denisrobichaud.com ↗	69 match 1 shared topics	Denis J.-J. Robichaud – Associate Professor, University of Notre Dame	en	education WordPress	robotsllmsaihumans	emailphone	none
natashawarikoo.com ↗	69 match 1 shared topics	Natasha Warikoo \| Stern Professor in the Social Sciences, Dept of Sociology, Tufts University	en	education WordPress	robotsllmsaihumans	emailphone	none
csualum.net ↗	69 match 1 shared topics	Alumni Association \| Colorado State University	en	education WordPress	robotsllmsaihumans	emailphone	none
csualum.org ↗	69 match 1 shared topics	Alumni Association \| Colorado State University	en	education WordPress	robotsllmsaihumans	emailphone	none
nazaninshahrokni.com ↗	69 match 1 shared topics	Dr Nazanin Shahrokni – Associate Professor of International Studies at Simon Fraser University	en	education WordPress	robotsllmsaihumans	emailphone	none
joshuafdoyle.com ↗	68 match 1 shared topics	Joshua F. Doyle \| Sociology, Purdue University	en	education WordPress	robotsllmsaihumans	emailphone	none
zsigae.com ↗	67 match 1 shared topics	Elizabeth Zsiga \| Professor of Linguistics at Georgetown University	en	education WordPress	robotsllmsaihumans	emailphone	none
joshuafinnell.com ↗	67 match 1 shared topics	Joshua Finnell – Associate University Librarian Colgate University	en	education WordPress	robotsllmsaihumans	emailphone	none
jonathanhaidt.com ↗	67 match 1 shared topics	Social psychologist - Author - Professor \| Jonathan Haidt	en	education WordPress	robotsllmsaihumans	emailphone	none
bardaaup.org ↗	67 match 1 shared topics	Bard AAUP – Online home for The Bard College Chapter of the American Association of University Professors	en	education WordPress	robotsllmsaihumans	emailphone	none
lucilemaertens.com ↗	66 match 1 shared topics	Lucile Maertens – Associate Professor in International Relations	en	education WordPress WooCommerce	robotsllmsaihumans	emailphone	none
ayolaniyonu.com ↗	66 match 1 shared topics	Ayobami Laniyonu, Ph.D. – Assistant Professor, University of Toronto	en	education WordPress	robotsllmsaihumans	emailphone	none
johannaoksala.com ↗	66 match 1 shared topics	Johanna Oksala – Arthur J. Schmitt Professor of Philosophy, Loyola University Chicago	en	education WordPress	robotsllmsaihumans	emailphone	none
samershehata.com ↗	66 match 1 shared topics	\| Samer S. Shehata \| – Colin Mackey and Patricia Molina de Mackey Associate Professor	en	education WordPress WooCommerce	robotsllmsaihumans	emailphone	none
natecheek.com ↗	66 match 1 shared topics	Nathan Cheek – University of Maryland	en	education WordPress	robotsllmsaihumans	emailphone	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.