```xml <result> <column_name>Occupation: Software &amp; IT</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, strongly against multiplicative. The column contains percentage values (0.00% to 1.44%) representing the proportion of each entity in the Software &amp; IT occupation category - this is an intensive property (a share or proportion at a point). While the values are expressed as percentages, they represent absolute proportions of a population distribution, not growth rates or ratios that compound. Summing these percentages across entities would be meaningless (you cannot add "0.94% Software workers" + "1.31% Software workers"), but averaging them to find typical occupation representation is valid. This is intensive_additive: a level measure where mean is the natural aggregator. </reasoning> <table_type_conflict>false</table_type_conflict> </result> ```