Piccardi et al. 2025 does not undermine the convergence of null results from algorithm-level manipulations because it tests a different intervention type and its key result loses significance under population reweighting.
Piccardi et al. (2025) tested LLM-based content-targeted reranking of AAPA content, a different intervention from the algorithm-on vs. algorithm-off comparison tested by Gauthier et al. (2026) and Guess et al. (2023).
The Piccardi et al. supplementary reweighting to match the X user population rendered the post-treatment Reduced Exposure effect non-significant.
The convergence of null results from algorithm-level manipulations across Facebook, Instagram, and X represents a consistent pattern across platforms and research teams.
Is the literature really agreed that defects of kind K bias inferences in direction B, or is the bias direction itself contested?
Does study S actually have defect D, or is the description of S inaccurate?
Is the expected magnitude of the bias from D large enough to overturn S's reported effect, or is the effect robust to plausible bias corrections?
Has S (or a follow-up study) performed a robustness check or sensitivity analysis that addresses defect D directly?
Is this critique applied consistently — i.e., would it apply to studies on the other side of the debate that share the same defect kind K?
Is H supported by independent studies that do not share defect D, such that S's defect does not undermine H itself?