Pre-algorithmic confounders including homophily, partisan identity strength, and cable TV are documented to be quantitatively larger contributors to polarization than algorithmic ranking in available studies, though their precise share of post-2012 growth is not established.

[NARROW-VARIANT] Iyengar et al. (2012) shows affective polarization was substantial in the 1980s, establishing that partisan identity strength predates social media. Bakshy et al. (2015) found that user choice (homophily) is the larger contributor to ideological exposure filtering than algorithmic ranking on Facebook. Eady et al. (2019) found most Twitter users are exposed to substantial cross-cutting content, and Eady et al. (2023) found foreign-influence exposure concentrated in already-polarized minorities with no attitude change. Therefore (narrowed), Pre-algorithmic confounders including homophily, partisan identity strength, and cable TV are documented to be quantitatively larger contributors to polarization than algorithmic ranking in available studies, though their precise share of post-2012 growth is not established.

• Iyengar et al. (2012) shows affective polarization was substantial in the 1980s, establishing that partisan identity strength predates social media.
• Bakshy et al. (2015) found that user choice (homophily) is the larger contributor to ideological exposure filtering than algorithmic ranking on Facebook.
• Eady et al. (2019) found most Twitter users are exposed to substantial cross-cutting content, and Eady et al. (2023) found foreign-influence exposure concentrated in already-polarized minorities with no attitude change.

These are challenges this argument’s reasoning pattern must still withstand. Answering them on Isonomia strengthens the argument.

• Is there a plausible alternative hypothesis that has not been considered or that would explain the facts at least as well?Open
• Could the facts be jointly explained by a conjunction of weaker causes rather than a single dominant H?Open
• Could the body of facts F itself be an artifact of selection, measurement, or reporting bias rather than a real phenomenon needing causal explanation?Open
• Does H actually explain the full body of facts F, or only a salient subset?Open
• Are the criteria used to judge H 'best' (scope, simplicity, mechanism, prior probability) appropriate for this domain, and are they applied consistently across the alternatives?Open

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