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54match
jayhendon.com
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0 co-citations1 shared tech1 shared categoryeconomy
52match
devisdecet.com
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0 co-citations1 shared tech1 shared categoryeconomy
52match
elena-stella.com
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0 co-citations1 shared tech1 shared categoryeconomy
52match
manan-patel.com
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0 co-citations1 shared tech1 shared categoryeconomy
51match
jasmiths.com
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0 co-citations1 shared tech1 shared categoryeducation-industry
51match
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0 co-citations1 shared tech1 shared categoryeconomy
50match
julianapinillos.com
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0 co-citations1 shared tech1 shared categoryeconomy
50match
juliaselgrad.com
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0 co-citations1 shared tech1 shared categoryfinancial-industry
50match
jasminehao.com
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0 co-citations1 shared tech1 shared categoryeconomy
50match
jasondebacker.com
Jason DeBacker
0 co-citations1 shared tech1 shared categoryeconomy
50match
javierbianchi.com
Javier Bianchi
0 co-citations1 shared tech1 shared categoryeconomy
50match
jaypgarg.com
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0 co-citations1 shared tech1 shared categoryeconomy
50match
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50match
jie-duan.com
Jie Duan/洁 段
0 co-citations1 shared tech1 shared categoryeconomy
50match
jinlinwei.com
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0 co-citations1 shared tech1 shared categoryeconomy
50match
jo-peace.com
Global Trade Portal
0 co-citations1 shared tech1 shared categoryeconomy
50match
hamidyahyaei.com
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0 co-citations1 shared tech1 shared categoryfinancial-industry
50match
henrikwiig.com
Henrik Wiig
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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.