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52match
mantleadorned.com
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49match
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49match
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1 co-citations1 shared tech1 shared categorybooks-and-literature
44match
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1 co-citations1 shared tech1 shared categorybooks-and-literature
44match
corywilcox.com
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43match
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1 co-citations1 shared tech1 shared categorybooks-and-literature
42match
janelouisecurry.com
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1 co-citations1 shared tech1 shared categoryfiction
42match
janefirebaugh.com
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1 co-citations0 shared tech1 shared categoryfiction
42match
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1 co-citations1 shared tech1 shared categorybooks-and-literature
42match
aragonbooks.com
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1 co-citations0 shared tech1 shared categorybooks-and-literature
42match
dickeydavisbooks.com
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1 co-citations0 shared tech1 shared categorybooks-and-literature
42match
dickeydavis.com
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1 co-citations0 shared tech1 shared categorybooks-and-literature
42match
georgetoolan.com
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1 co-citations1 shared tech1 shared categorybooks-and-literature
42match
gerryfinan.com
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1 co-citations0 shared tech1 shared categoryart-and-photography
42match
booksyoumaylike.com
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41match
hldyer.com
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0 co-citations1 shared tech1 shared categorybooks-and-literature
41match
100topbook.com
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1 co-citations1 shared tech1 shared categorybooks-and-literature
40match
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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.