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Ellis Scharfenaker's avatar

I recently read Carlos Chavez’s line that “the credibility revolution succeeded by developing research designs where the identifying assumptions were embedded in the structure of the data rather than imposed by the researcher.” (https://carloschavezp29.substack.com/p/why-macro-never-had-a-credibility).

To me, that framing captures the wrong turn that keeps the social sciences in permanent crisis. The identifying assumptions never vanish, they just move upstream into measurement, classification, sampling, and the choice of what even counts as "treatment," "unit," and "outcome." But many applied social scientists still convince themselves that data are somehow self-interpreting facts rather than acknowledge that they become evidence only through theory, and are already theory-laden categories. If the theory is thin or wrong, the categories can be wrong, and the statistics will deliver precise answers to a misconceived question. The fix is not to pretend we can design away judgment, it is to make judgment explicit, contestable, and honestly limited in scope.

Hollis Robbins's avatar

The hard sciences are fine. The danger, as I write here, is that bad social science threatens higher ed and has made universities particularly vulnerable to AI disruption. https://hollisrobbinsanecdotal.substack.com/p/attention-is-all-you-need-to-bankrupt

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