Monday, September 4, 2017

Reducing hate as a measure of church health

Pastor Jeremy Smith of the blog Hacking Christianity does a bit of data crunching and comes up with a proposal related to the question of how do we define whether a church is healthy? Smith confines his discussion to the United Methodist Church.

First the data crunching. Smith compares the number of hate groups in proportion to population in each state and notes which jurisdiction (region, there are five across America) that state is in. Yes, the highest percentage of hate groups is in the Southeast Jurisdiction.

Smith carefully says correlation is not causation. We cannot tell if church attendance (in a conservative region) inspires hate groups or whether hate groups encourage church attendance.

Instead, Smith asks different questions: Should churches be doing more to combat organized hate in their communities? Should they engage groups in their own backyard? Will it make a difference?

Smith uses the example of Daryl Davis, a black musician who has been in the news lately. When Davis encounters a Klansman he tries to get to know them and for them to know him. Over the years 200 of his new friends have left the Klan, many times giving Davis their robe.

Smith takes this one more step. He reminds us we value what we count. At the moment United Methodist churches must report dollars in the offering plate, attendance (“nickels and noses” as a commenter puts it), number of people involved in mission projects, and other similar numbers. But those numbers do not include engagement with hate groups. Perhaps the denomination should ask churches to report hate group contact hours (5 people as counter protesters at a rally for 2 hours is 10 contact hours).

Some of the comments are insightful. Others dismiss Smith because he got his data on hate groups from the Southern Poverty Law Center. I’ve responded to those who think the SPLC is corrupt and useless. Then as now a group that doesn’t want to be seen as a hate group will try to discredit any organization that says they are.

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