```xml <result> <column_name>Race: Hispanic</column_name> <family>additive</family> <subtype>intensive_additive</subtype> <confidence>0.92</confidence> <requires_user_confirmation>false</requires_user_confirmation> <sum_meaningful>false</sum_meaningful> <signed>false</signed> <reducers> <primary_reducer>mean</primary_reducer> <valid_reducers>mean, min, max, median, distribution</valid_reducers> <excluded_reducers>sum (summing percentages across entities is meaningless), product (not a ratio measure)</excluded_reducers> <average_semantics>arithmetic_mean</average_semantics> </reducers> <inner_outer_measure> <inner_measure_reducer>min</inner_measure_reducer> <outer_measure_reducer>max</outer_measure_reducer> </inner_outer_measure> <multiplicative_partner_derivable>false</multiplicative_partner_derivable> <reasoning> Table-type gate: DIMENSION with no sequence axis strongly favors additive family. The column contains percentage values (0.58% to 11.42%) representing demographic composition shares - these are intensive properties of each entity (proportion Hispanic in each geographic unit or group). While expressed as percentages, these are absolute demographic levels, not growth rates or ratios that compound. Summing percentages across different entities is meaningless (you cannot add "10% Hispanic" + "5% Hispanic" to get "15% Hispanic"); the natural aggregation is averaging to find mean demographic composition. This is a classic intensive_additive measure: an absolute level that describes a property at a point, where mean is the appropriate reducer for rollup. </reasoning> <table_type_conflict>false</table_type_conflict> </result> ```