The largest available experiments consistently measure near-zero effects of algorithmic ranking on affective polarization, and while these point estimates cannot be linearly scaled to 12-year cumulative effects, no validated compounding model exists to justify scaling them above the 10% threshold either.

[NARROW-VARIANT] Three Meta experiments show effects on affective polarization of less than 0.03 standard deviations over 1.5 months, which is small compared to 1.1 standard deviations of nationwide polarization growth over 40 years. Even if these small experimental effects were linearly scaled over 12 years, the resulting aggregate would remain far below the 10% threshold of observed affective polarization change stipulated by the framing. The effects of both Facebook and Instagram deactivation on affective and issue polarization were all precisely estimated and close to zero in a sample of over 35,000 users. Therefore (narrowed), The largest available experiments consistently measure near-zero effects of algorithmic ranking on affective polarization, and while these point estimates cannot be linearly scaled to 12-year cumulative effects, no validated compounding model exists to justify scaling them above the 10% threshold either.

• Three Meta experiments show effects on affective polarization of less than 0.03 standard deviations over 1.5 months, which is small compared to 1.1 standard deviations of nationwide polarization growth over 40 years.
• Even if these small experimental effects were linearly scaled over 12 years, the resulting aggregate would remain far below the 10% threshold of observed affective polarization change stipulated by the framing.
• The effects of both Facebook and Instagram deactivation on affective and issue polarization were all precisely estimated and close to zero in a sample of over 35,000 users.

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

• Does the operational measure of F in the sample actually capture F as it is meant in the population-level claim?Open
• Is the sample actually representative of the target population on the dimensions that matter for F (demographics, behavior, time period, platform mix)?Open
• Is the sample large enough to support the precision (margin m) being claimed?Open
• Does the conclusion stay within the population P from which S was drawn, or does it overreach (different country, different time period, different platform)?Open
• Was the sample drawn or recruited in a way that systematically biases the proportion of F (e.g., volunteer bias, opt-in panels, attrition)?Open

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No arguments cite this one yet — no one has built on or contested it. That is an absence of engagement, not a finding of soundness. Build on or contest it on Isonomia to change that.