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54match
julielcasey.com
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5 co-citations2 shared tech2 shared categorybooks-and-literature
51match
electagraham.com
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4 co-citations3 shared tech2 shared categoryfiction
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jl-cole.com
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2 co-citations4 shared tech2 shared categoryfiction
49match
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4 co-citations2 shared tech2 shared categorybooks-and-literature
48match
jejack.com
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2 co-citations3 shared tech2 shared categoryfiction
48match
gerrihill.com
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2 co-citations3 shared tech2 shared categorybooks-and-literature
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3 co-citations2 shared tech2 shared categoryfiction
47match
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3 co-citations2 shared tech2 shared categorybooks-and-literature
47match
makilien.com
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jmbeal.com
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3 co-citations2 shared tech2 shared categorybooks-and-literature
46match
janellrhiannon.com
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3 co-citations2 shared tech2 shared categorybooks-and-literature
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2 co-citations2 shared tech2 shared categoryfiction
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janaoliver.com
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2 co-citations2 shared tech2 shared categoryfiction
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allanmowat.com
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3 co-citations2 shared tech2 shared categoryfiction
46match
adriandebeer.com
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1 co-citations3 shared tech2 shared categorybooks-and-literature
45match
bancroftandgreene.com
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2 co-citations2 shared tech2 shared categorybooks-and-literature
45match
kennethovermanbooks.com
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3 co-citations1 shared tech2 shared categorybooks-and-literature
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jasmineangell.com
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6 co-citations3 shared tech2 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.