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A new study investigates potential biases in AI-driven research evaluation, revealing a significant disconnect between perceived prestige and practical implementability. The research, detailed in the working paper “Prompt Engineering as Epistemic Instrumentation,” analyzed how AI models, including ChatGPT, Claude, and DeepSeek, assess research papers through various lenses. Using prompts designed to evaluate papers along dimensions like ‘Cold (prestige),’ ‘Implementation (12-month deployability),’ ‘Transformative (paradigm-shifting),’ and ‘Toolmaker (methodological infrastructure),’ the researchers found a near-total lack of overlap (0-6%) between AI-identified ‘prestigious’ and ‘implementable’ research among the 2,500+ papers analyzed. This suggests AI systems may prioritize theoretical impact or novelty over immediate real-world application when evaluating research. The study’s methodology, data corpus, and analysis code are publicly available on GitHub, fostering open discussion and further investigation into AI’s role in shaping research priorities. Initial discussion surrounding the paper began on Reddit: [Reddit Post: https://old.reddit.com/r/artificial/comments/1odixpv/wip_paper_prompt_engineering_as_epistemic/]