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61match
kennethovermanbooks.com
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3 co-citations0 shared tech2 shared categorybooks-and-literature
61match
joaniestrulowitz.com
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3 co-citations0 shared tech2 shared categorybooks-and-literature
60match
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3 co-citations0 shared tech1 shared categoryart-and-photography
58match
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2 co-citations0 shared tech2 shared categorybooks-and-literature
57match
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3 co-citations0 shared tech2 shared categorybooks-and-literature
56match
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3 co-citations0 shared tech2 shared categorybooks-and-literature
56match
barbaraanndailey.com
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56match
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3 co-citations0 shared tech2 shared categorybooks-and-literature
56match
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3 co-citations0 shared tech2 shared categorybooks-and-literature
52match
jejack.com
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2 co-citations0 shared tech2 shared categoryfiction
52match
jindeluong.com
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2 co-citations0 shared tech2 shared categoryfiction
52match
greenandbeyondmag.com
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2 co-citations0 shared tech2 shared categoryfiction
52match
ap27cybersolutions.com
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2 co-citations0 shared tech2 shared categorybooks-and-literature
52match
balancebothworlds.com
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2 co-citations0 shared tech2 shared categoryfiction
51match
kellyealsop.com
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3 co-citations0 shared tech1 shared categorybooks-and-literature
51match
marciamar.com
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3 co-citations0 shared tech1 shared categorypoetry
51match
adrienneraphel.com
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3 co-citations0 shared tech1 shared categorybooks-and-literature
49match
kenziemack.com
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2 co-citations0 shared tech2 shared categoryfiction

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.