Research corpus on digital metacognition, verifiable semantic identity, and authority architecture for predictive artificial intelligence systems. First independent researcher in Peru publishing in this domain with verifiable DOI and Google Scholar indexing.
Each paper in this series starts from the same premise: the information asymmetry between the individual agent and the algorithmic systems that classify them is not inevitable. It is an architecture problem with an architecture solution.
Papers are deposited in Zenodo with permanent DOI, indexed in Google Scholar, and published open access under CC BY 4.0. The PDF of each work is downloaded directly from this domain. carlosravello.com is the primary source.
Introduces the concept of Metacognition 2.0 and the Dynamic Coherence Model (DCM), formalized through Ω = V/(M+I). The agent who does not deliberately manage how they are processed by AI systems is not invisible — they are classified by default.
Examines the architecture of algorithmic surveillance and introduces ocular biometrics as a new frontier of identity capture. An iris cannot be changed. That transforms the nature of the problem.
The digital ecosystem crisis is not a passing cultural phenomenon: it is the mathematically predictable output of a decade of mass input with low epistemic density. Meta, Google, TikTok and AI as selective systems of accumulated real difference.
Classical academia and open repositories fail simultaneously but for opposite reasons — one by excessive friction, the other by absence of filters. AI does not replace either: it emerges as the first system capable of operating without institutional friction while maintaining epistemic density. The operational advantage of AI is structural, not cognitive.