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100match
electjoesteere.com
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1 co-citations4 shared tech1 shared categorypolitics
85match
jennifermenlopark.com
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85match
kenziejonesjohnson.com
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1 co-citations2 shared tech1 shared categorypolitics
85match
kylebarry.com
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1 co-citations2 shared tech1 shared categorypolitics
85match
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1 co-citations2 shared tech1 shared categorypolitics
85match
jamiemcelwain.com
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85match
janicezale.com
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1 co-citations2 shared tech1 shared categorypolitics
85match
apebloc.com
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1 co-citations2 shared tech1 shared categorypolitics
85match
appomattoxdems.org
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1 co-citations2 shared tech1 shared categorypolitics
85match
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85match
cooperforlake.com
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85match
cosaspensadas.com
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85match
devpurohit.com
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85match
electgregboyle.com
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1 co-citations2 shared tech1 shared categorycivic-affairs
85match
electdenisewright.com
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1 co-citations2 shared tech1 shared categorypolitics
85match
electryancallahan.com
Callahan for Harris Township 2026
1 co-citations4 shared tech1 shared categoryelections
85match
elijahmack.com
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1 co-citations2 shared tech1 shared categorypolitics
85match
general-butler.com
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1 co-citations2 shared tech1 shared categorypolitics

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.