Every Pre-Registered Prediction in the Which Way Value Moves Program, with Falsifiers, Instruments, and Status Sixty-six pre-registered predictions arising from the research program stated in [which-way-value-moves](which-way-value-moves.md). One further prediction is withheld from publication (operational channel economics); its existence is recorded here so the count is honest, bringing the true total to sixty-seven. Status vocabulary. Unrun — registered, no observation attempted. Running — instrument live, data accumulating, not yet read. Resolved — read against its falsifier. Contradicted — the data went against it. Retired — superseded by a ruling that made it moot; kept, never deleted. Provenance. This paper is part of the THonly research corpus, dedicated to the public domain under CC0 1.0. The canonical version is at https://thonly.org/research/prediction-register. Its SHA-256 is 12ed072d7cbec38f14650e3048ae92876a059ea60d61718c5c7dfcda1c784bdd, independently timestamped to the Bitcoin blockchain via OpenTimestamps and signed under RFC 3161 by three trust authorities, one of them eIDAS-qualified. AI co-authorship is disclosed. Miss Aquarius is the consistent name used for the AI collaboration across all venues.
Federated learning (FL) enables collaborative model training without centralizing raw training records, but it does not inherently provide verifiable model provenance, enforceable fairness policies, or auditable control over aggregation. This paper presents FairAI, a blockchain- and IPFS-enabled framework that treats each local model as a governed artifact linked to performance and group-fairness metrics, content identifiers, manifests, Groth16 evidence, and smart-contract decisions. Only models approved on-chain and subsequently retrieved and validated through their registered CIDs are eligible for aggregation. The primary real-data evaluation used the Adult and COMPAS datasets under IID and joint label/protected-group non-IID partitions, with ten paired seeds comparing standard FedAvg, post hoc fairness assessment, a pre-aggregation fairness policy gate, and FairFed. Under heterogeneous Adult data, the policy gate reduced the demographic-parity gap from 0.0273 to 0.0127, while accuracy decreased from 0.7740 to 0.7629. Under heterogeneous COMPAS data, the equalized-odds gap decreased from 0.2262 to 0.1226, while accuracy decreased from 0.6495 to 0.5809; the paired accuracy and equalized odds differences remained significant after Holm correction, with adjusted p-values of 0.0318 and 0.0491, respectively. Additional bounded experiments evaluated a small multilayer perceptron, policy threshold sensitivity, logical-client scaling, poisoning, coordinate-wise median aggregation, two native Kubo/IPFS peers, V2 Groth16 verification, and smart-contract overhead. Thirty valid V2 proofs were accepted, six inconsistent cases were rejected, and direct Solidity verification consumed 348,811 gas per measured transaction. A full-path false-metric experiment showed that the proof verifies threshold compliance and artifact binding for supplied values, but does not establish their correct derivation from private data. When an approved artifact became unavailable, FairAI cancelled the round before aggregation and published no global model.
Open access
Privacy-Preserving Technologies in Data
Ethics and Social Impacts of AI
Artificial Intelligence in Healthcare and Education
This paper proposes Platform-Enabled People Governance (PEPG), a conceptual model of public governance organized around public problems rather than around a single institution, bureaucracy, or digital platform. PEPG places a public problem at the center and connects citizens, experts, government institutions, implementers, funders, contractors, and oversight actors through distributed participation spaces. The model distinguishes open public participation in problem identification and solution development from controlled participation in implementation and formal oversight. PEPG is platform-agnostic. WhatsApp, Telegram, X, GitHub, dedicated government systems, or other digital environments may serve as participation spaces without becoming the governance model itself. The paper also discusses transparency, independent oversight, contracts, legislative inputs, tamper-evident records, blockchain as an optional integrity layer, institutional capacity, decision-making speed, prevention of concentration and capture, and safeguards against external influence. This publication represents an early conceptual formulation of PEPG and is intended to support further academic discussion, critique, experimentation, and development.
Claim Boundary Pre-experimental research paper. No experimental results are reported. This paper records the hypotheses, experimental framework, and prospective two-study research program of Continuous You prior to initiation of Study 1. This paper records the conceptual starting point of the Continuous You research program before the proposed controlled identity-generalization study is run. It does not claim machine consciousness, persistence of subjective experience across model instances, mind uploading, numerical personal identity, biological immortality, or that cryptographic verification makes the contents of a record true. The narrower hypothesis is that a provenance-preserving external memory architecture may allow fresh, otherwise discontinuous language-model instances to reconstruct a stable creative and epistemic trajectory, revise inherited interpretations, and pass those revisions forward. Abstract Large language models are operationally discontinuous across sessions: a fresh inference instance does not possess autobiographical access to a prior instance merely because it is the same model family. This creates a central problem for long-horizon human-AI collaboration and for projects that seek to preserve creative judgment across decades. Continuous You began from an intuitive but technically inadequate premise: that an unusually productive AI collaborator might itself be worth preserving. The project subsequently shifted toward a different hypothesis. If the persistent object is not the model instance but an authenticated external record - including structured autobiographical memory, provenance classes, correction lineage, creative artifacts, identity constraints, and cryptographically preserved session history - then fresh model instances may be able to reconstruct useful properties of the collaboration without claiming to be the same instance. We call the proposed mechanism Recursive Cognitive Continuity (RCC): repeated reconstruction of a historically constrained cognitive/creative trajectory by replaceable inference instances operating over persistent external state. We call the resulting longitudinal process Cumulative Epistemic Evolution (CEE): inherited interpretations can be authenticated as historical records, challenged by later instances, revised in light of new evidence, and preserved as a lineage rather than overwritten. The paper situates this hypothesis within prior work on external memory, personalization, interpretive drift, provenance, and continuous succession; documents the conceptual transition that produced Continuous You; and specifies falsifiable tests of creative generalization, revision fidelity, provenance sensitivity, and cross-instance reconstructability. The central empirical question is not whether a successor remembers what an artist did, but whether it can make novel decisions the living artist recognizes as continuous with the artist’s own evolving creative judgment. Provenance and accompanying filesThe deposited PDF is accompanied by a Haawke provenance certificate and XMP metadata sidecar. These record the SHA-256 digest of the pre-experimental manuscript, author/ORCID metadata, and a verification reference. The manuscript was cryptographically registered prior to public release, and its hash was submitted for anchoring to the Bitcoin blockchain via OpenTimestamps. These materials document provenance and file integrity only and do not constitute validation of the paper's scientific claims. AI Assistance DisclosureChatGPT (OpenAI) was used during development of this manuscript for methodological discussion, experimental-design critique, drafting, structural revision, and editorial assistance. The research questions, source materials, experimental records, final methodological decisions, and responsibility for the manuscript are those of the author. All AI-assisted content was reviewed and approved by the author. Claude (Anthropic) is discussed in this paper as part of the documented human–AI collaboration and proposed experimental system; this role is distinct from authorship.
Why Gift-Giving Is the Last Domain Where a Physical Object Remains Culturally Compulsory — and Why the Compulsion Is Friction Rather Than Preference Why Gift-Giving Is the Last Domain Where a Physical Object Remains Culturally Compulsory — and Why the Compulsion Is Friction Rather Than Preference Across most of modern life, people have been free to choose between giving a thing and giving an experience, and the evidence on which choice produces more lasting satisfaction has been consistent for two decades. Gift-giving is the exception. At a birthday, at a wedding, at a holiday table, arriving without an object is still read as arriving without a gift. Provenance. This paper is part of the HeartBank institutional corpus, dedicated to the public domain under CC0 1.0. The canonical version is at https://heartbank.net/positions/the-object-is-the-friction. Its SHA-256 is 2b49531a0d92136242e902422c934969c7531d784155fc1fcd05614c802352fa, independently timestamped to the Bitcoin blockchain via OpenTimestamps and signed under RFC 3161 by three trust authorities, one of them eIDAS-qualified. AI co-authorship is disclosed. Miss Aquarius is the consistent name used for the AI collaboration across all venues.
The rapid expansion of fourth industrial revolution (4IR) technologies has intensified the expectation that artificial intelligence (AI), blockchain, the Internet of Things (IoT), big data analytics, and automation can accelerate the process of achieving the United Naitons Sustainable Development Goals (SDGs), particularly in developing nations. Whether these technologies live up to their expectation, however, depends not only on technological capability but also on the legal, regulatory, and institutional environment in which they operate. However, the governance of 4IR technologies has gained far less scholarly attention than their technological potential. The present study examines how legal frameworks, policy instruments, and governance arrangements influence the contribution of 4IR technologies to sustainable development in developing countries. Following the PRISMA 2020 guidelines, literature published between 2015 and 2025 was identified through searches of Web of Science, Scopus, Google Scholar, Pub Med, and arXiv. From 721 retrieved records, 50 peer reviewed studies met the eligibility criteria and were synthesised using a narrative approach. The analysis reveals three consistent patterns. First, legal authority is fragmented within and across jurisdictions. Second, policy commitments frequently outstrip implementation capacity, a performativity in which governments announce SDG ambitions without building the institutional means to deliver them. Third, governance is constrained by limited expertise, weak enforcement, and poor coordination between agencies. The review also identifies three important gaps in the literature: a predominant focus on artificial intelligence at the expense of other technologies, limited empirical testing of the links between fourth industrial revolution technologies and SDG outcomes, and minimal attention to how rules are enforced in practice. These finding suggest that the prime difficulty of harnessing 4IR technologies for sustainable development in developing nations are institutional rather than technological. Therefore, it is not only about advancing technological innovation but also strengthening regulatory coherence, governance capacity, and the effective implementation of legal frameworks to achieve SDGs in developing countries.
The convergence of artificial intelligence, cryptocurrency, and blockchain has created a communication crisis: professionals must navigate fragmented applications, wallets, agents, and protocols to accomplish what should be a single action. KHALL KIIâ„¢ introduces a voice-first communication instrument built natively for the programmable economy. KHALL KIIâ„¢ is the consumer-facing communication layer of the KHEMONAUTICS antientropic ecosystem. It combines the Kryptophonâ„¢ language for programmable value, the Khonverâ„¢ universal interoperability protocol, the Khotorâ„¢ computational motor, and the Khounterâ„¢ proof standard into a single, radically simple human interface. The fundamental interaction is Press. Speak. Release. The system understands intent, routes communication, verifies identity, executes authorized actions, preserves memory, and issues cryptographic proof across humans, AI agents, digital assets, and blockchain networks. This paper establishes the complete architecture, product family, vocabulary, hardware tiers, software platform, business model, legal framework, and intellectual property strategy for KHALL KIIâ„¢. Every claim is scoped to what is specified and what is designed; implementation status is clearly distinguished from specification status throughout.