Epistemic Infrastructure Assurance: A Statutory Framework for Metric Integrity in Public Decision-Making

This paper advances a social-epistemological account of research metrics as epistemic infrastructure: socio-technical systems whose outputs function as authoritative signals within institutional practices of evaluation. Citation counts, download statistics, h-index scores, and rankings are not merely descriptive indicators but inputs into decisions about promotion, funding, and reputational standing. Their epistemic status therefore depends not only on accuracy in a narrow sense, but on the conditions under which they are produced, validated, and contested. The paper introduces the concept of reliability-for-reliance as a normative standard for such systems. An output meets this standard where it is sufficiently accurate, auditable, and contestable to justify its use in consequential decisions without independent re-verification. Failures of epistemic infrastructure need not involve deception or breakdown; they may arise through ordinary system defects, aggregation practices, or institutional reliance on outputs that lack adequate provenance and contestability. These are cases of silent epistemic degradation, in which signals appear plausible while no longer warranting trust. Drawing on the structural lessons of the Post Office Horizon scandal as a case of institutional over-reliance on system-generated outputs, the paper argues that similar, though less severe, epistemic risks are present in the governance of research metrics. The core problem is not only that systems can be wrong, but that institutions may lack the procedural conditions required to recognise and correct error. This generates asymmetries of knowledge and power that bear comparison to forms of epistemic injustice. The paper situates these concerns at the intersection of social epistemology, philosophy of technology, and information ethics. It argues that epistemic infrastructure requires institutionalised forms of auditability, provenance, and contestability, and that current practices in research evaluation fall short of these requirements. The analysis is developed alongside a policy framework grounded in data protection and governance principles, but its primary contribution is philosophical: to characterise the epistemic conditions under which system-generated signals can legitimately function as knowledge in institutional decision-making.