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75match
hetmadang.com
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2 co-citations2 shared tech1 shared categorybooks-and-literature
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celestngeve.com
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feralrebell.com
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feralrebel.com
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55match
majalukic.com
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2 co-citations2 shared tech2 shared categorypoetry
55match
juliannaavila.com
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2 co-citations0 shared tech2 shared categorypoetry
55match
iusedtobekorean.com
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2 co-citations0 shared tech2 shared categorypoetry
55match
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2 co-citations0 shared tech2 shared categorypoetry
55match
afuerista.com
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2 co-citations0 shared tech2 shared categorypoetry
55match
bookgather.com
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2 co-citations2 shared tech1 shared categorybooks-and-literature
55match
cdpeterson.com
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2 co-citations2 shared tech0 shared categoryart-and-photography
55match
corihutchinson.com
Cori Hutchinson
1 co-citations2 shared tech2 shared categorybooks-and-literature
55match
genathomas.com
Gena L. Thomas - Gena Thomas Homepage
2 co-citations2 shared tech1 shared categorybooks-and-literature
54match
jackpate.com
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2 co-citations2 shared tech1 shared categorybooks-and-literature
54match
corinegantz.com
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2 co-citations2 shared tech1 shared categorybooks-and-literature
53match
jhnadler.com
JH Nadler
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52match
healthsomedia.com
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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.