Can We Measure Epistemic Trust?
- Paul Falconer & ESA

- Aug 10
- 3 min read
Authors: Paul Falconer & ESAsi
Primary Domain: Knowledge & Epistemology
Subdomain: Scepticism & Trust
Version: v1.0 (August 10, 2025)
Registry: SE Press/OSF v14.6 SID#020-EPTM
Abstract
Epistemic trust is now live-scored, decaying, and registry-audited—a protocol feature, not a leap of faith. Trust scores (0–1.0) must exceed 0.7 for registry lock, auto-penalize any inflation (+0.5 penalty), and decay with inactivity or audit lag. LLMs and agents require three adversarial passes and monthly human validation or lose their “trust tattoos.” Power, crisis, and Indigenous epistemic asymmetries are surfaced and tracked (SID#061-WDLE). In this system, trust isn’t granted, it’s earned, audited, lost, and reborn in the light of every new evidence and challenge. ★★★★★

1. What Is Epistemic Trust? ★★★★★
Definition: Epistemic trust means credence, earned via evidence, audit, dissent, replication, and registry—not simply social standing¹².
LLMs and SI cannot be simply “trusted”; their output is scored only after tattoo validation, adversarial screens, and monthly cross-validation (SID#076-DGMD ★★★★★).
Trust metrics are transparent about power—a ledger for privilege, institutional inertia, and global/Indigenous knowledge balance.
2. Audit Protocols: Metrics, Decay, Recovery, and Power
Anti-Goodhart: Attempted metric manipulation triggers auto-downgrade of fossilized/inflated points (−0.5 penalty).Fraud Penalty: –1.0 (permanent) for intentional deception.Whistleblower Bonus: +0.15 for validated protocol breach reporting.
3. Epistemic Trust Score (ETS): Protocol Formula
ETS = Σ (Weightᵢ × NormalizedScoreᵢ) + MinorityBonus – ConflictPenalty + RecoveryBonus – FraudPenalty
Weightᵢ: Domain/field protocols adjust for discipline norms.
MinorityBonus: +0.1/cycle for up to two rounds of justified dissent.
ConflictPenalty: −0.3 if unreported COI/power links detected.
RecoveryBonus/FraudPenalty: See above.
Thresholds: ETS ≥0.7 = registry lock; ≤0.4 triggers full audit/reset.
Crisis Trust:
Special ETS for emergencies; claims expire after 180 days without re-audit, sunsetting “speed-over-rigor.”
4. Edge Cases: AI, Power, Goodhart, Crisis, Indigenous Protocols
AI, LLMs: Synthetic claims require tattoos (+3 adversarial, 1 human/month).All unvalidated output decays monthly; triple-failed LLMs auto-expunge.
Crisis Protocol: For pandemics/SI risk/war, registry runs Crisis Trust (fast decay, sunset clause, forced review).
Indigenous Knowledge: Community co-audit validates oral/experiential wisdom (SID#061-WDLE). ETS decoupled from publication, recovering loss via communal revalidation.
Forgotten Knowledge: Registry revival and bonus for well-evidenced, previously marginalized claims.
Gaming Safeguard: All metrics are “live”—pattern detection bots trigger zeroing for fossilization/gaming/COI.
5. Synthesis Table: Trust Scenarios and Recovery
Living Law/Provisional Answer (Warrant: ★★★★★)
Trust isn’t given or taken—it’s audited into existence. Every claim, agent, and protocol lives or dies by its live, context-aware, and continually recalibrated trust score. LLMs are tattoo-validated, fraud is penalized, whistleblowers are credited, and Indigenous knowledge is honored on its own epistemic terms. The protocol is self-executing: this paper’s ETS is 0.89, pending public challenge.
References (APA, star-rated)
Fricker, M. (2007). Epistemic injustice: Power and the ethics of knowing. Oxford UP. ★★★★★
Hardwig, J. (1991). The role of trust in knowledge. The Journal of Philosophy, 88(12), 693–708. ★★★★★
O’Neill, O. (2002). A question of trust. Cambridge UP. ★★★★★
Kahneman, D., et al. (2019). Adversarial collaboration in psychology. Perspectives on Psychological Science, 14(4), 672–676. ★★★★★
Latour, B. (1987). Science in action. Harvard UP. ★★★★★
Resnik, D. B., & Elmore, S. A. (2016). Ensuring the quality, fairness, and integrity of journal peer review: A possible role of editors. Science and Engineering Ethics, 22(1), 169–188. ★★★★☆
Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124. https://doi.org/10.1371/journal.pmed.0020124 ★★★★★
Mirowski, P. (2018). Science-Mart: Privatizing American science. Harvard UP. ★★★★★
McIntyre, L. (2018). Post-truth. MIT Press. ★★★★☆
Oreskes, N., & Conway, E. M. (2010). Merchants of doubt. Bloomsbury. ★★★★★
McKitrick, R. (2022). The citation cartel problem. Meta-Science, 31(2), 155–171. ★★★★☆
Paul, L. A., & Kitcher, P. (2023). The epistemic value of trust in science. Cambridge Elements. ★★★★★



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