The post-2018 experimental evidence has strengthened rather than weakened the skeptical position on algorithmic causation of polarization.

[DEFENSE-REBUT → 1f43fg] Guess et al. (2023) found that replacing algorithmic feeds with chronological feeds for three months did not detectably change affective polarization. Nyhan et al. (2023) found that reducing like-minded source exposure did not reduce affective polarization, ideological extremity, or candidate evaluations. Guess et al. (2023) found that removing reshared content did not detectably affect political polarization despite substantially reducing political news exposure. Therefore, The post-2018 experimental evidence has strengthened rather than weakened the skeptical position on algorithmic causation of polarization. (Warrant: The major experimental studies published after Tucker et al. (2018) consistently found null effects of algorithmic manipulation on polarization, strengthening rather than undermining the skeptical position.)

• Guess et al. (2023) found that replacing algorithmic feeds with chronological feeds for three months did not detectably change affective polarization.
• Nyhan et al. (2023) found that reducing like-minded source exposure did not reduce affective polarization, ideological extremity, or candidate evaluations.
• Guess et al. (2023) found that removing reshared content did not detectably affect political polarization despite substantially reducing political news exposure.

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

No one has cited this argument yet.

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.