Individual-level effects observed in platform experiments, while not reaching the 10% threshold in direct 3-month tests, demonstrate that algorithmic curation produces measurable polarization-relevant attitude changes that may contribute to population-level polarization growth over longer time horizons.

[NARROW-VARIANT] The Facebook deactivation experiment found measurable reductions in political polarization from just four weeks of non-use among a sample of 2,743 users. Over 200 million US adults actively use Facebook, so even small per-person effects aggregate to substantial population-level impact. The effect persisted in reduced post-experiment Facebook use, suggesting cumulative rather than transient influence. Therefore (narrowed), Individual-level effects observed in platform experiments, while not reaching the 10% threshold in direct 3-month tests, demonstrate that algorithmic curation produces measurable polarization-relevant attitude changes that may contribute to population-level polarization growth over longer time horizons.

• The Facebook deactivation experiment found measurable reductions in political polarization from just four weeks of non-use among a sample of 2,743 users.
• Over 200 million US adults actively use Facebook, so even small per-person effects aggregate to substantial population-level impact.
• The effect persisted in reduced post-experiment Facebook use, suggesting cumulative rather than transient influence.

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.