```xml <result> <column_name>Gender: Female</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 of change)</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> DIMENSION table with no sequence axis strongly priors against multiplicative. The column contains percentage values (1.19%, 10.54%) representing the proportion of females in each entity - this is an intensive property (a composition ratio) at a fixed point, not a rate of change over time. While the "%" unit might suggest multiplicative, these are absolute demographic levels where summing across entities is meaningless but averaging is natural. The high distinct_fraction (96%) confirms these are entity-specific composition measurements. Classified as intensive_additive with mean as the primary reducer. </reasoning> <table_type_conflict>false</table_type_conflict> </result> ```