Funniest predatory-journal pitch ever

2026-02-15 · Source: Statistical Modeling, Causal Inference, and Social Science · Field: Science & Research — Research Methodology & Innovation, Mathematics & Computational Sciences · Depth: Fundamental Awareness, quick

Summary

Dr. Gelman received an email invitation from "Alex" of an editorial team to contribute to a theme issue on "Statistical Methods for ** journal **." The initial pitch cited Gelman's article on statistical crises in science and its relevance to clinical neuropsychology, specifically asking about the role of Bayesian methods. After a quick check of the organization's website, which appeared legitimate, Gelman performed a Google search and discovered the journal was predatory. Upon informing "Alex" of this, the editorial team member surprisingly asked for recommendations of others who might contribute. Gelman refused, but "Alex" continued to send follow-up emails, persistently inviting participation despite the explicit rejection and identification of the journal as a scam.

Key takeaway

For AI Scientists considering publication opportunities, you should always conduct thorough due diligence on unfamiliar journals, even if the initial outreach appears personalized and professional. Do not rely solely on a journal's website; cross-reference with independent sources to confirm legitimacy. Your reputation is tied to your publications, so avoid predatory journals that undermine research integrity.

Key insights

Predatory journals use persistent, personalized, and seemingly legitimate outreach to solicit contributions from researchers.

Principles

• Verify journal legitimacy beyond initial appearances.
• Persistent follow-ups are a red flag for predatory practices.

Method

Researchers should cross-reference journal invitations with independent searches to identify predatory publishers, even if initial checks seem positive.

In practice

• Always Google unfamiliar journals and publishers.
• Be wary of "theme issue" invitations from unknown sources.

Topics

• Predatory Journals
• Academic Publishing
• Statistical Methods
• Research Ethics
• Email Scams

Best for: AI Scientist, Research Scientist, AI Researcher

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Editorial summary, takeaway, and curation by AIssential. Original article published by Statistical Modeling, Causal Inference, and Social Science.