Cross-national divergence remains informative because US-specific experiments that should capture algorithm-structure interactions also find null effects on affective polarization.

[DEFENSE-REBUT → m7amq6] Guess et al. (2023), Nyhan et al. (2023), and Gauthier et al. (2026) all tested algorithmic effects within the US political context and found null effects on affective polarization. If algorithmic amplification patterns vary across countries as Huszár et al. (2022) found, then cross-national divergence combined with US-specific experimental nulls jointly constrain the algorithmic contribution in the US. Therefore, Cross-national divergence remains informative because US-specific experiments that should capture algorithm-structure interactions also find null effects on affective polarization. (Warrant: When both cross-national and US-specific experimental evidence point away from a significant algorithmic effect, the interaction hypothesis lacks empirical support from either strand.)

• Guess et al. (2023), Nyhan et al. (2023), and Gauthier et al. (2026) all tested algorithmic effects within the US political context and found null effects on affective polarization.
• If algorithmic amplification patterns vary across countries as Huszár et al. (2022) found, then cross-national divergence combined with US-specific experimental nulls jointly constrain the algorithmic contribution in the US.

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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