The Journal of Prompt-Engineered Philosophy Or: How I Started to Track AI Assistance and Stopped Worrying About Slop

• URL: http://arxiv.org/abs/2511.08639v1
• Date: Thu, 13 Nov 2025 01:01:17 GMT
• Title: The Journal of Prompt-Engineered Philosophy Or: How I Started to Track AI Assistance and Stopped Worrying About Slop
• Authors: Michele Loi,
• Abstract summary: Article analyzes how incentives discourage transparency in precisely the work where it matters most.<n>Traditional venues cannot resolve this tension through policy tweaks alone, as the underlying prestige economy rewards opacity.<n>It proposes an alternative publishing infrastructure: a venue outside prestige systems that enforces mandatory disclosure, enables reproduction-based review, and supports ecological validity through detailed documentation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Academic publishing increasingly requires authors to disclose AI assistance, yet imposes reputational costs for doing so--especially when such assistance is substantial. This article analyzes that structural contradiction, showing how incentives discourage transparency in precisely the work where it matters most. Traditional venues cannot resolve this tension through policy tweaks alone, as the underlying prestige economy rewards opacity. To address this, the article proposes an alternative publishing infrastructure: a venue outside prestige systems that enforces mandatory disclosure, enables reproduction-based review, and supports ecological validity through detailed documentation. As a demonstration of this approach, the article itself is presented as an example of AI-assisted scholarship under reasonably detailed disclosure, with representative prompt logs and modification records included. Rather than taking a position for or against AI-assisted scholarship, the article outlines conditions under which such work can be evaluated on its own terms: through transparent documentation, verification-oriented review, and participation by methodologically committed scholars. While focused on AI, the framework speaks to broader questions about how academic systems handle methodological innovation.

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