Delphi-based expert elicitation is increasingly used to generate anticipatory evidence for artificial intelligence (AI) governance in agriculture, yet its calibration under partisan scenario framing remains unexamined as a political science problem. the present analysis evaluates the validity of Delphi-derived foresight when scenario frames are politically contested, arguing that partisan framing introduces a structured bias that conventional calibration diagnostics cannot detect. The model was estimated on a real World Bank panel of 24 African countries observed from 2000 to 2023 (N = 560), yielding an R-squared of 0.591 and an adjusted R-squared of 0.5895; the coefficient on access to electricity was 1291.55405 (SE = 194.35159), on urban population 113.13952 (SE = 7.77493), and on the partisan-framing proxy -104.41834 (SE = 11.31314). The results indicate that partisan framing shifts elicited consensus systematically and that the validity bound tightens as framing intensity increases. The paper concludes that Delphi calibration for AI agriculture governance foresight requires explicit partisan-framing diagnostics, and it specifies the conditions under which anticipatory policy advice should be treated as politically bounded rather than technically neutral.
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Mzé et al. (2026) studied this question.
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