Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We model the dynamics of discrimination and show how its evolution can identify the underlying source. We test these theoretical predictions in a field experiment on a large online platform where users post content that is evaluated by other users on the platform. We assign posts to accounts that exogenously vary by gender and evaluation histories. With no prior evaluations, women face significant discrimination. However, following a sequence of positive evaluations, the direction of discrimination reverses: women's posts are favored over men's. Interpreting these results through the lens of our model, this dynamic reversal implies discrimination driven by biased beliefs.