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58match
malikhenry.com
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3 co-citations3 shared tech1 shared categorycareers
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jiahaoliu.com
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55match
joanmanrubia.com
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2 co-citations3 shared tech1 shared categorycareers
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2 co-citations3 shared tech1 shared categorycareers
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55match
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baldurlogi.com
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cooperstepanian.com
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2 co-citations3 shared tech1 shared categorycareers
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elainecui.com
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2 co-citations3 shared tech1 shared categorycareers
54match
filipalacerda.com
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2 co-citations3 shared tech1 shared categorycareers
54match
hlodver.com
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2 co-citations3 shared tech1 shared categorycareers
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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.