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Intersectionality as Inquiry & Praxis – Making the invisible visible — 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
donthideitflauntit.org ↗	66 match 1 shared topics	DHIFI - Celebrating Differences Visible & Invisible	en	education WordPress	robotsllmsaihumans	emailphone	none
barcworkshop.org ↗	65 match 1 shared topics	Making the revolution irresistible (T.C. Bambara)	en	education Weebly WooCommerce	robotsllmsaihumans	emailphone	none
bardmathcircle.org ↗	64 match 1 shared topics	BARD MATH CIRCLE – Making Math Fun	en	education WordPress	robotsllmsaihumans	emailphone	none
ctreading.org ↗	64 match 1 shared topics	Connecticut Reading Association – A State Affiliate of the International Literacy Association	en	education WordPress WooCommerce	robotsllmsaihumans	emailphone	none
5schoolsstrong.org ↗	64 match 1 shared topics	5 Schools Strong – Preserving the Integrity of Tiverton Schools	en	education WordPress WooCommerce	robotsllmsaihumans	emailphone	none
envirosoc.org ↗	64 match 1 shared topics	SECTION ON ENVIRONMENTAL SOCIOLOGY – Reshaping the Study of Sociology	en	education WordPress WooCommerce	robotsllmsaihumans	emailphone	none
elevateos.org ↗	64 match 1 shared topics	ElevateOS — The visibility layer for tutoring and admissions	en	education	robotsllmsaihumans	emailphone	none
1lifebetter.com ↗	64 match 1 shared topics	1LifeBetter – Making one life better at a time	en	education WordPress	robotsllmsaihumans	emailphone	none
davisteaching.com ↗	64 match 1 shared topics	What's Next... - writing at the intersection of education, math, and justice...and maybe some other stuff too	en	education WordPress WooCommerce	robotsllmsaihumans	emailphone	none
eqofoundation.org ↗	64 match 1 shared topics	EQO FOUNDATION – Balancing the Opportunity Equation	en	education WordPress WooCommerce	robotsllmsaihumans	emailphone	none
datapraxis.net ↗	64 match 1 shared topics	Understanding Data: Praxis and Politics – Critical data literacies	en	education WordPress	robotsllmsaihumans	emailphone	none
esef-ng.org ↗	64 match 1 shared topics	E-based Socio Educational Foundation (ESEF) – Breaking the Walls of Higher Education & Social Strata	United States~ en	education WordPress WooCommerce	robotsllmsaihumans	emailphone	none
annaleegood.com ↗	64 match 1 shared topics	Annalee G. Good – Intersections between education research, evaluation, and communities	en	education WordPress	robotsllmsaihumans	emailphone	none
btoied.com ↗	64 match 1 shared topics	Beating the Odds in Education – Closing the Achievement Gap	en	education WordPress WooCommerce	robotsllmsaihumans	email phone	none
katiethecurious.com ↗	64 match 1 shared topics	Katie the Curious – Now that's interesting…	en	education WordPress	robotsllmsaihumans	emailphone	none
decodingtheivorytower.net ↗	63 match 1 shared topics	Decoding the Ivory Tower	en	education WordPress WooCommerce	robotsllmsaihumans	emailphone	none
enrollindy.org ↗	63 match 1 shared topics	Enroll Indy – Streamlining the school enrollment process in Indianapolis	United States~ en	education WordPress	robotsllmsaihumans	emailphone	none
bcstechnology.org ↗	63 match 1 shared topics	#BCSTech – BCS Instructional Technology	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.