```xml <result> <column_name>Income: $50K–$75K</column_name> <family>additive</family> <subtype>intensive_additive</subtype> <confidence>0.85</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 is DIMENSION with no sequence axis, making multiplicative highly implausible. The column represents percentage shares of population in a specific income bracket (demographic composition). Sample values (0.00%, 0.82%, 1.39%, etc.) are percentage levels that describe the intensity/composition at each entity. Summing these percentages across different geographic entities is meaningless (you cannot add "0.82% from region A" to "1.39% from region B"); the natural rollup is to average them or examine their distribution. This is a classic intensive_additive measure: an absolute level (percentage composition) where mean is the appropriate aggregation. </reasoning> <table_type_conflict>false</table_type_conflict> </result> ```