models say more than their teams mean to say.
hiv, malaria, allocation across countries and populations. i rebuild the logic under model outputs until the buried commitments are inspectable: what was measured, what was borrowed, what got smoothed over.
i understand the machinery but don’t own the first answer. the work shows which conclusions hold when those assumptions move, and which don’t.