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62match
abookwithoutpages.com
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4 co-citations8 shared tech1 shared categorybooks-and-literature
59match
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4 co-citations5 shared tech1 shared categorybooks-and-literature
59match
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4 co-citations8 shared tech1 shared categorybooks-and-literature
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3 co-citations5 shared tech1 shared categorybooks-and-literature
56match
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Cheyenne Berandi - Home
4 co-citations3 shared tech1 shared categoryfiction
54match
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Fazayal Shabbir – Author
4 co-citations7 shared tech1 shared categorybooks-and-literature
54match
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ANUSHÉ BABAR HALL - home
3 co-citations3 shared tech1 shared categorybooks-and-literature
52match
authorarunkrishnan.com
authorarunkrishnan.com Books - authorarunkrishnan.com
3 co-citations6 shared tech1 shared categorybooks-and-literature
52match
annamlewis.com
Anna M Lewis – Author & Illustrator
4 co-citations5 shared tech1 shared categorybooks-and-literature
52match
bettinarestrepo.com
Bettina Restrepo - Bettina Restrepo
4 co-citations2 shared tech1 shared categorybooks-and-literature
52match
avabudavari.com
Ava Budavari – Writer/Activist
3 co-citations5 shared tech1 shared categorybooks-and-literature
52match
cynmackley.com
Cyn Mackley – Mystery Writer & Tech Whisperer
3 co-citations7 shared tech1 shared categorybooks-and-literature

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:

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.