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Critical Metascience

Critical metascience takes a step back to question some common assumptions, approaches, problems, and solutions in metascience.

Newsletter · By Mark Rubin · Official site

Indexed issues, last 90 days
10
Latest publication
Sep 28, 2026
Audience
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Earliest in this view
Jul 13, 2026

Latest issues

  1. Issue · Sep 28, 2026

    Transparency, Replication Rate Targets, and Direct Replications (opens the original)

    Excerpt

  2. Issue · Sep 7, 2026

    Is Replication Rare? 🔸 Registered Report Timelines 🔸 Open Science and Trust 🔸 P-Hacking and Publication Bias (opens the original)

    Excerpt

  3. Issue · Aug 24, 2026

    AIMOS Presentation 🔸 Mindless Transparency 🔸 Publication Bias 🔸 Top 10 Posts (opens the original)

    Excerpt · Positive tone

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    AIMOS PresentationI enjoyed making a presentation titled “Questioning Some Metascience Assumptions” to the Association for Interdisciplinary Meta-Research and Open Science (AIMOS) at Victoria University of Wellington, New Zealand on 13th August. There’s a recording of my presentation below and a copy of my slides here. (The recording appears to cut out towards the end, but bear with it and it will get back on track.)Many

  4. Issue · Aug 19, 2026

    Theory Aversion, Methodolatry, and Scientism in Psychology and Metascience: Comment on Cornejo and Rojas (2026) (opens the original)

    Excerpt

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    There’s a new book out on “Theory and Model Building in Psychology,” edited by Fabian Hutmacher and Alexander Wendt. You can download a copy here.<img alt="" class="si

  5. Issue · Aug 10, 2026

    Null Results and File Drawers 🔸 The Cooptation of Science Reform 🔸 The Weaponization of Transparency (opens the original)

    Excerpt

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    Null Results and File DrawersBartosz Maćkiewicz and colleagues conducted a survey of 50 experimental philosophers in order to investigate the prevalence, causes, and potential solutions to publication bias and the file-drawer problem:Contrary to the common assumption that journals reject null results, we found that most projects halt during early data analysis or presentation stages. The primary drivers for this attrition are resource constraints and competing priorities rather than statistical

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