Rotational discovery over a non-accumulating liveness signal — a local-discovery layer with no recipient-facing surface, no recipient aggregate and no impression count, in which admission is a predicate over circulation rather than a score, order is a publicly recomputable rotation, and the turn belongs to the giver. The liveness signal named in this paper's title is an admission predicate, not a weighted input to a ranking function. We state this first because the paper's whole content follows from it: a weighted input produces an order, an order is a rank, and a rank is the thing this design exists to do without. Local discovery — routing a person to a nearby business they do not yet know — is presently solved by ranked, purchasable surfaces. That solution has a structural bias toward scale which is mechanical rather than malicious: an auction allocates visibility to the highest bidder per acquired customer, and the highest bidder is reliably whoever has the largest lifetime value, the best measurement, the cheapest capital, and the widest geography over which to amortise creative production. A single-location business is priced out of the discovery layer by construction. Reputation systems built to correct this — consumer review platforms above all — have been captured repeatedly, and we argue the capture follows from a shared property rather than from bad management: reviews are fungible, and they are aggregated into a per-recipient total, so both the fake-review market and the placement-upsell business have something to attach to. We specify a discovery layer with no such total. Admission is a predicate over circulation — a rate, not a stock, derived from how often a participant's balance returns to zero — evaluated as a boolean with amplitude excluded, so that the smallest circulating operator is admitted exactly as much as the largest, and so that admission cannot be accumulated toward. Order is a publicly recomputable rotation. The turn belongs to the giver, arising at the moment they spend from a forward-only, locality-restricted account, from which it follows that the system has no recipient-facing surface and therefore nothing to sell to the parties it routes people toward. Where a real relational path exists — someone the viewer has themselves thanked has thanked this recipient — that single named hop is shown instead; a path is one act by one named person and cannot sum. We are explicit about what this does not achieve. The design does not prevent concentration. It prevents compounding. Givers will still choose the familiar, so the outcome distribution may remain heavy-tailed; what the design removes is the return edge of the feedback loop, because there is no recipient total for an outcome to accumulate into. A ranked system has a ratchet — visible, therefore chosen, therefore more visible. This one does not. That is a smaller claim than "no gradient," and it is the one we can defend. We are equally explicit about the costs. Quality degrades relative to ranking: find me the best pho in town is a question this system permanently refuses, and we answer only pho near me, by distance. Discovery becomes intermittent, because admission is bound to a named operator's presence rather than to premises. The isolation inequity documented elsewhere in this corpus — those whose kindness is less legible circulate less and are therefore seen less — is not repaired here. And the design contains exactly one number, the liveness window, which must be published, global and rarely changed, because a window tuned per recipient or per district is a ranking knob wearing a predicate's clothes. Finally we note that the routing primitive is not new to this corpus and we do not claim it. Steward-Routed Alms (July 2026) already published rotation-as-router for monastic invitation, on the explicit ground that no evaluative metric may exist anywhere in the system. What is new here is the substitute for ordination. In the monastic case admission is a durable institutional status; in a commercial setting there is no ordination, and the mechanism needs some criterion that admits without ranking and cannot be accumulated toward. The liveness predicate is that substitute, and supplying it is what generalises a monastic routing rule into a discovery layer. Offered defensively to the commons under CC0. Keywords: unranked discovery, rotational routing, non-accumulating reputation, liveness signal, popularity-gradient-free ranking, giver-side discovery, local commerce, sortition, rotating savings and credit association, impression-free advertising alternative, defensive publication. --- 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/rotation-over-liveness. Its SHA-256 is 072c25d396930668cc6fe1a503615f108ba81ddd2e68415adba5933b8ddd169b, 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.
In the era of digital revolution many contemporary events that changed the world were shaped through the internet. Nowadays, the emergence of internet of things (IoT), combining physical objects with virtual networks is expected to have even more influence. This new 'decentralised' structure in the world raises questions such as power, governance and the notion of democracy online. The aim of this paper is to investigate these notions. We have taken the examples of Bitcoin and Wikipedia and examined their decision-making process. Our analysis has found some inconsistencies in their policies, that are in contradiction with democracy and consensus principles of governance. Starting from our findings, we present further improvements that can be used to achieve more democracy and equity in the digital context.
Decentralized social protocols such as Nostr introduce a new paradigm for user-generated content (UGC) in the Web3 era, where content production, dissemination, and reward mechanisms operate without centralized governance. This paper presents one of the first large-scale empirical analyses of Nostr, based on 22.3 million user events collected from four major publicly accessible relays. Guided by three research questions, we examine (1) the temporal and spatial distribution of user participation, (2) the structural characteristics of decentralized UGC networks, and (3) thematic and incentive patterns in content creation and Zap-based rewards. Our analysis shows rapid growth followed by long-tail stabilization, while the interaction network remains highly modular and loosely connected, indicating fragmented yet persistent communities. Embedding-based clustering of textual posts identifies ten clusters on several topics: technical discussions, ideological debates, personal expression, community coordination, and media sharing, highlighting a hybrid ecosystem of social and technical discourse. We further find that knowledge-oriented content in Clusters 1 and 5 receives higher Zap engagement, suggesting the socialization of a primarily technical infrastructure. These findings advance the understanding of decentralized multimedia ecosystems by linking network decentralization with observed participation and engagement patterns in the absence of centralized moderation.
Decentralized Autonomous Organizations (DAOs) offer a novel approach to collectively governing projects through a democratic mechanism facilitated by blockchain. DAOs allow members to put forward and vote on proposals, thereby shaping the organization’s future. However, low voter turnout is common in DAO decision-making, particularly in large and active DAOs, where the high volume of proposals makes it unrealistic to expect members to track all proposals. Abstentionism threatens the voting system's effectiveness and the legitimacy of the results. We consider that recommender systems can help boost voter engagement. This article details the design of a recommendation approach tailored to aid DAO members in identifying proposals of interest, alongside its implementation and evaluation. The design accommodates the domain constraints that render off-the-shelf recommendation approaches inadequate, namely that proposals are short-lived and can only be recommended while available for voting. To the best of our knowledge, this is the first study to examine recommendation in DAO governance. To carry out our research, we have compiled a dataset, made publicly available, covering 12 of the most active DAOs. We compare a baseline, specifically designed to accommodate DAO-specific constraints, against a range of recommendation techniques. The findings confirm that personalized recommenders can often anticipate voting preferences, significantly outperforming the baseline. In turn, given the limitations of offline evaluation, an online evaluation using A/B testing would also be needed to fully assess their impact on participation. We also discuss how their implementation must carefully incorporate fairness and transparency to ensure community trust and adoption. We believe that proposal recommender systems in DAOs not only could drive engagement improvements similar to those observed in other online collaborative projects, but can also provide insights for collective governance settings beyond blockchain, such as cooperative organizations or participatory budgeting platforms.
Elena Baninemeh, Marre Slikker, Katsiaryna Labunets, Slinger Jansen
Purpose This study aims to investigate the impact of cybersecurity vulnerabilities on the effective implementation of distributed ledger technologies (DLTs), addressing a critical gap in the existing literature. This research seeks new insights into the detection and mitigation of specific attacks, such as selfish mining and Sybil attacks, contributing to a deeper understanding of cybersecurity risk assessment in DLT applications. Design/methodology/approach This study uses a mixed-methods approach, using a literature review combined with method engineering. Data were collected from an extensive database of known security threats, documented attacks on DLTs and associated countermeasures. The proposed method was evaluated through three case studies, with each organization applying the security risk assessment method developed in this study. Findings The results of this study reveal that the proposed security risk assessment method effectively identifies and addresses cybersecurity threats specific to distributed ledger applications. Case studies demonstrate that the method enables organizations to systematically evaluate and mitigate risks, offering evidence that comprehensive countermeasures can significantly enhance security. These findings confirm the practicality of the proposed method and reveal new patterns in organizational responses to cybersecurity threats in distributed ledger environments. Originality/value This research offers a novel perspective on the intersection of cybersecurity and DLTs, providing valuable insights into risk assessment frameworks tailored for this domain. This study’s findings contribute to the advancement of cybersecurity practices in distributed ledger applications, highlighting critical areas for future research and practical guidelines for organizations aiming to enhance their cybersecurity posture.
This study examines PUPS, a representative Bitcoin ecosystem project, to elucidate the success mechanisms of Web3 meme projects. We test three hypotheses: (H1) community sentiment and social media virality constitute the fundamental drivers of meme asset valuation; (H2) core participants accumulate positions at low prices and distribute at peak valuations; (H3) meme diffusion is predominantly driven by internal imitation, significantly outweighing external marketing effects. Applying event study methodology, social network analysis, and the Bass diffusion model to social media and on-chain data, our findings support all hypotheses, revealing a ”propagation–sentiment–trading” pathway. We identify a distinctive ”community fingerprint” comprising 348 original holders and 5,036 6-core addresses, characterizing them as both community stabilizers and hype catalysts. This pattern illustrates the paradox of ”economic recentralization” within technically decentralized systems. Paradoxically, the founder's public assertion that ”everything will eventually go to zero” evolved into a cultural ritual that reinforced community consensus. This study concludes by proposing a ”meme financialization” framework, offering novel perspectives for understanding ”Attention as Capital”, ”Consensus as Value”, and ”Narrative as Asset” in Web3 ecosystems.
M. Shunmugasundaram, S. Gangadharan, Rina Dave, Saraswati Kala · 5 authors
One of the main reasons for the surge of Shadow AI is the widespread use of AI technology in businesses. One of the major drivers behind the increasing prevalence of Shadow AI is the integration of AI technology in enterprise environments. As Artificial Intelligence (AI) becomes ubiquitous in the enterprise, Shadow AI has surged in the number of organizations using AI out of control or without authorization. This research paper explores how Shadow AI has developed from the traditional Shadow IT concept in an autonomous enterprise context where AI use is decentralized, agents are used to automate processes, and the decision-making is machine-driven. The paper discusses governance blind spots like hidden AI integrations, non-human identities, lack of explainability, AI drift, and autonomous risks. It uses a qualitative approach with review, analysis, and synthesis to create a shadow AI governance framework. This framework includes AI discovery, telemetry monitoring, zero-trust controls, explainability, and continuous auditability. The findings show that existing approaches to IT governance are insufficient for adaptive ecosystems of AI, and enterprise governance must be continually monitored, documented, and tracked; resilient to cyber threats; compliant with regulations; and ensure digital trust.
In approximately the year 2000, the author conceived and partially implemented a multi-layered community economic system centered on Shibuya, Tokyo. The system integrated real-time human broadcasting, local media production, a unified community coupon currency, youth-driven cultural monitoring, and digital education — years before the terminology of "DAO," "Web3," "UGC," or "creator economy" existed. This paper documents that original conception, analyzes its structural architecture, and demonstrates its direct lineage to the author's current work: the Hikari Currency (光貨) ecosystem and the ECHO AI Artist platform. The Shibuya system was not understood by contemporaries. It is understood now.
Sohel Akhtar, Murat Karakuş, Rukiye Savran Kiziltepe
Smart contracts are a fundamental building block of blockchain platforms such as Ethereum, yet their development and auditing require specialized expertise and remain highly error-prone. The immutability of deployed smart contracts significantly amplifies the consequences of coding mistakes and security flaws. Recent advances in Large Language Models (LLMs) have shown promise in automating software development and code analysis tasks; however, the reliability of LLM-generated smart contracts and their effectiveness in vulnerability auditing, particularly for Solidity, remains insufficiently explored. In this paper, we present a systematic and automated evaluation pipeline to comparatively assess the performance of open-source LLMs in two critical phases: (i) smart contract generation from natural language specifications, and (ii) smart contract auditing for vulnerability detection. We benchmark multiple open-source models under consistent experimental settings and analyze their correctness, security awareness, and robustness against insecure outputs. Our findings expose significant performance gaps across models and tasks, revealing strengths and limitations of current open-source LLMs in supporting secure smart contract development. This study provides practical insights for researchers and practitioners seeking to apply LLMs to blockchain programming and security assessment.
Li Li, X R Wang, Hong‐Ning Dai, Fang Li · 6 authors
As a paradigm for encouraging users to contribute data spontaneously, mobile crowdsensing (MCS) has received considerable attention recently. It is crucial to evaluate the truthfulness of MCS data by proper truth discovery mechanisms. Although recent truth discovery schemes can determine truthful information, they either provide limited privacy preservation or have heavy computation and communication overheads. Moreover, most of them are not resilient to malicious faults and active attacks. To tackle the above problems, we propose two fault-tolerant and privacy-preserving truth discovery solutions. Our first scheme is mainly used for scenarios with a relatively stable number of users, where participants do not frequently join or leaves. Integrating ring signature with the perturbation technique, we design an anonymous and privacy-preserving truth discovery scheme, namely RsAnonTD, which can achieve privacy preservation and resist active attacks. To address the challenge with dynamically changed workers, we devise a multi-client inner product functional encryption scheme with a lightweight zero-knowledge proof protocol (namely McFeKDeTD) for defending against active attacks. The security analysis shows that both schemes can preserve the privacy of sensory data, weights, and estimated truths while resisting active attacks, thereby guaranteeing fault tolerance. Extensive experiments demonstrate that our designs achieve superior performance than other schemes in terms of accuracy, convergence speed, and system overheads. For example, compared with the state-of-the-art approach RPTD-II, which has a security level comparable to ours, our proposed schemes, RsAnonTD and McFeKDeTD, reduce the computational overheads approximately by 98% and 69%, respectively.
This replication package contains the data and scripts used in this empirical study, including the LLM-based semantic validation pipeline, the observed practice extraction process, and all figures from the research questions (RQ1–RQ5). ERC_Observed_Practicess.xlsx: workbook of observed practices ERC_Observed_Practices_Process_Review.xlsx: Phases to generate the workbook of observed practices Other supplementary materials: Essential data files (data/) results_semantic_validation.json: 11,559 issues classified by LLM (substantive, category, justification) sample_manual_review_updated.csv: ~400 manually reviewed entries for LLM quality validation eips_labels.csv / ercs_labels.csv: PR metadata from Ethereum repositories for status evolution analysis (RQ5) Scripts (scripts/) 01: scrapes the official ERC list from ethereum.org 02 : filters the dataset for ERC mentions via regex 03: classifies issues via Gemini (substantive + category) 03: removes duplicates from the validation JSON 03: merges LLM results with issue metadata 04: extracts observed practices per ERC via Gemini, cross-referenced with official specs 05: fetches GitHub labels and generates ERC status evolution figure (RQ5) 06: generates all quantitative figures (RQ1–RQ4)
Maksym W. Sitnicki, Олена Шатілова, Nikita Smohorzhevskyi
The growth of the knowledge economy requires new models enabling consulting firms to convert expertise into venture capital capabilities within Web 3.0 ecosystems. Existing research rarely explains how knowledge-based consultancies transform into institutional investors with scalable investment strategies and measurable performance. This study aims to develop an original theoretical and applied framework explaining the transition of a Web 3.0 consulting company into a venture capital institution through quantitative forecasting, governance mechanisms, and diversified investment design. The proposed concept integrates organizational maturity assessment, financial modeling, investment governance, and scenario analysis into a unified venture transition framework for knowledge-economy firms. The core research question addresses how a knowledge-economy consulting company can operationalize its transition into venture capital management within the Web 3.0 ecosystem. Using PEMM analysis, gap analysis, Gantt charts, RACI matrices, market sizing (TAM/SAM/SOM), financial forecasting, and scenario modeling, this paper proposes a phased framework for venture fund structuring, investment strategy formulation, and 5-year performance projections—directly applied to Solus Agency’s context to demonstrate practical pathways for capturing value in this high-growth, high-risk domain. The empirical basis combines venture datasets, company-level indicators, and proprietary Solus Agency statistics, including 180+ venture funds, 160+ private investors, 46 fundraising projects, and USD 13.8 million attracted for clients. Quantitative modeling shows that a diversified USD 50 million fund may generate projected profits of USD 120 million under a negative scenario, USD 200 million in the baseline scenario, and USD 290 million in an optimistic scenario, corresponding to expected multipliers between 2.4× and 5.8×. Portfolio valuation is forecast to increase from USD 20.6 billion to USD 54.6 billion, demonstrating substantial sensitivity to allocation strategy and market conditions. The proposed Solus Agency subfund achieves an expected total return of USD 36.38 million, a gross multiplier of 3.64, a net multiplier of 3.11, a gross IRR of 52.05%, and a LP net IRR of 43.60%, indicating high projected efficiency despite elevated early-stage risks. Probability modeling identifies seed-stage allocations as the strongest contributor (USD 13.06 million projected profit) and demonstrates that diversification across AI, Web3, DeFi, and RWA segments reduces volatility while preserving growth potential. The scientific novelty lies in constructing an original framework quantitatively linking organizational maturity, consulting expertise, and venture performance indicators. The findings provide a transferable model for knowledge-economy firms seeking institutionalization as venture capital actors and support further research on quantitative venture strategies and Web 3.0 investment ecosystems.
Rohith John Jacob, Shane Jacob Sebastian, Varsha S Panicker, Vishnu Satish · 5 authors
Digital art marketplaces have been expanding rapidly, resulting in a greater focus on issues of ownership verification, copyright infringement, and content tampering. Current blockchain non-fungible token (NFT) systems only keep ownership data on the blockchain, with the media files stored off the blockchain, resulting in a lack of trust in the ownership of assets secured by NFTs. This work proposes a framework for verifying the ownership of digital art in a decentralized fashion, using invisible frequency domain watermarks, cryptographic hash functions, and artificial intelligence (AI)-based semantic checks. This results in a method for embedding ownership directly into the image being protected by an NFT, with the cryptographic fingerprint of the image stored on the blockchain for easy access. An AI-based semantic verification layer provides assurance that ownership can only be confirmed by performing consistent content checks. As has been verified through experimentation, the framework developed will allow owners of digital images to preserve ownership of their images and to securely and decentralized verify ownership.
[Purpose/Significance] The rapid evolution of artificial intelligence technologies from dialogue-based generation to autonomous task execution marks a paradigm shift with profound implications for library services. A new generation of AI agents, exemplified by the open-source project OpenClaw, can independently plan multi-step tasks, invoke external tools, operate computer interfaces through visual perception, and deliver structured work products with minimal human intervention. The shift from "answering questions" to "completing tasks" fundamentally challenges the traditional library service model. The model has long been based on the idea that librarians serve as the primary connection between information resources and users. Libraries worldwide are facing an increasing structural tension: their collections are expanding while their staffing levels are remaining constrained, resulting in unmet knowledge service demands. Agent technologies, with their capabilities for autonomous planning, tool invocation, environmental perception, and persistent memory, offer a potential pathway to address this gap. However, the library community currently lacks a systematic analytical framework through which to understand how this technology paradigm intersects with existing service architectures, governance requirements, and organizational structures. This study addresses this gap by providing both a conceptual framework for analyzing agent technologies in the library context and practical guidance for their implementation and governance, contributing to the broader discourse on intelligent library transformation as articulated in national science and technology development strategies. [Method/Process] This study employs a multi-method research design with OpenClaw as the primary analytical lens. The technical architecture analysis involves systematic examination of OpenClaw's publicly available documentation, GitHub source code repository, and official technical publications. Four core mechanisms are deconstructed in detail: the Computer Use Agent paradigm, which enables vision-driven interface operation through periodic screen capture, multimodal language model interpretation, and simulated mouse and keyboard actions; the local-first architecture with model-agnostic design, which maintains data sovereignty through a decentralized gateway-node topology while supporting flexible switching among multiple large language models; the Heartbeat mechanism, which transforms the agent from a passive responder into a proactive monitor through a condition-triggered self-inspection cycles; and the Model Context Protocol, an open standard for tool integration that enables any MCP-compliant agent to invoke standardized service capabilities. Case comparison analysis evaluates two contrasting platform approaches for supporting agent deployment in libraries - FOLIO Eureka, representing the next-generation Library Service Platform pathway with its microservice architecture, API gateway, and event-driven communication, and the Cloud Alliance's A-LSP, representing an agent-native design philosophy that positions intelligent agents as the core organizational principle of library service platforms. Policy document analysis examines the IFLA Guide on the Introduction of AI in Libraries, China's New Generation Artificial Intelligence Development Plan, the Data Security Law, and the Personal Information Protection Law, as well as regional policy experiments in agent technology promotion. Security incident case studies draw from the ClawHavoc supply chain attack, which compromised over 21 000 active instances; Cisco Talos security audits, which revealed prompt injection vulnerabilities; and CrowdStrike threat assessments, which identified misconfiguration risks that could transform agents into attack vectors. [Results/Conclusions] The study proposed a critical distinction between "narrow OpenClaw" (the specific open-source product and its derivative ecosystem) and "broad OpenClaw" (the agent technology paradigm it represents), arguing that libraries must engage strategically with both dimensions while avoiding the twin pitfalls of conflating technology trends with product procurement decisions or dismissing an entire paradigm based on the limitations of a single product. The narrow application analysis identified three viable deployment scenarios - personal productivity tools for librarians, information collection and subject monitoring, and reader-facing service prototyping - while documenting associated risks in technical stability, supply chain security, and regulatory compliance. The broad paradigm analysis revealed five structural impacts on libraries: diversification of service entry points through embedded integration, transformation from reactive response to proactive push services, evolution of reader information behaviors from search to delegation, disruption of commercial ecosystems including usage-based pricing models, and fundamental repositioning of libraries as knowledge infrastructure in the AI ecosystem. Four architectural prerequisites for agent deployment were identified: API openness, event-driven capabilities, permission governance, and observability, with insufficient system openness identified as the primary bottleneck that constrains implementation. Three differentiated implementation pathways were proposed with corresponding phased strategies. A comprehensive governance framework has been constructed encompassing six dimensions: system security with defense-in-depth measures, data governance and privacy protection aligned with national legislation, ethical standards addressing algorithmic bias and hallucination risks, copyright compliance addressing the ambiguity of agent-mediated access under existing licensing agreements, human-agent collaboration through a tiered oversight system, and standardization initiatives including library-specific MCP tool standards. The study also proposed institutional innovations such as "agent sandbox zones" that allow controlled experimentation in isolated environments. The research concluded that the highly structured and process-oriented nature of library workflows makes libraries a particularly suitable domain for agent technology adoption, but successful implementation depends on the coordinated advancement of technical readiness, governance maturity, and organizational change capacity. Limitations of this study include the nascent stage of actual agent deployment in libraries, which means the proposed frameworks await empirical validation. Future research directions include conducting empirical studies of library agent deployments, developing standardization pathways for cross-library agent collaboration, investigating copyright licensing adaptation mechanisms for agent-mediated access, and examining the long-term impact of agent technologies on the library profession and library science education.
Vahab Esfandani, Mohammad Amin Borghei, Sara Ravan Ramzani, Peter Konhaeusner · 6 authors
The digital economy has expanded organizations’ ability to source ideas, labor and capital through online participation, making crowdsourcing a strategic mechanism for innovation and problem solving. This chapter conceptualizes strategic crowdsourcing as a socio-technical system rather than ad hoc task outsourcing and synthesizes dispersed theory and evidence into a coherent framework for design and governance. It defines major typologies—micro-tasks, open innovation contests, co-creation, crowdfunding, internal crowdsourcing and citizen science—and situates them relative to outsourcing and open-source collaboration to clarify when each approach fits task uncertainty, required expertise and desired ownership of outputs. Building on open innovation, socio-technical systems and participatory governance perspectives, the chapter proposes an integrated model with five linked layers: contextual drivers; input configuration (task specification, crowd definition and call design); enabling infrastructure (platforms and technologies, including AI and blockchain-based mechanisms); process mechanisms (incentive design, validation and quality assurance, data governance and ethical/legal safeguards); and outputs/outcomes (innovation, organizational learning, governance effects and social value with feedback loops). Cross-sector illustrations from technology, healthcare, education, civic tech and sustainability highlight recurring trade-offs around motivation, quality control, fair compensation, privacy and confidentiality and intellectual property rights. The chapter also evaluates emerging hybrid human–AI crowdsourcing and decentralized autonomous organizations (DAOs), emphasizing that their benefits depend on transparent rules, accountable allocation of rewards and decision rights and human-in-the-loop oversight to mitigate bias, concentration of control and trust failures. Overall, strategic crowdsourcing is positioned as potentially democratizing when aligned with organizational goals and governed responsibly. It concludes by outlining research directions for comparative studies, cross-cultural analysis and regulation-aware design.
In approximately the year 2000, the author conceived and partially implemented a multi-layered community economic system centered on Shibuya, Tokyo. The system integrated real-time human broadcasting, local media production, a unified community coupon currency, youth-driven cultural monitoring, and digital education — years before the terminology of DAO, Web3, UGC, or creator economy existed. This paper documents that original conception, analyzes its structural architecture, and demonstrates its direct lineage to the author's current work: the Hikari Currency ecosystem and the ECHO AI Artist platform.
Matteo Vaccargiu, Sabrina Aufiero, Silvia Bartolucci, Ronnie de Souza Santos · 6 authors
Labels on platforms such as GitHub support triage and coordination, yet little is known about how well they align with code modifications or how such alignment affects collaboration across contributor experience levels. We present a case study of the Kubernetes project, introducing label-diff congruence - the alignment between pull request labels and modified files - and examining its prevalence, stability, behavioral validation, and relationship to collaboration outcomes across contributor tiers. We analyse 18,020 pull requests (2014--2025) with area labels and complete file diffs, validate alignment through analysis of over one million review comments and label corrections, and test associations with time-to-merge and discussion characteristics using quantile regression and negative binomial models stratified by contributor experience. Congruence is prevalent (46.6\% perfect alignment), stable over years, and routinely maintained (9.2\% of PRs corrected during review). It does not predict merge speed but shapes discussion: among core developers (81\% of the sample), higher congruence predicts quieter reviews (18\% fewer participants), whereas among one-time contributors it predicts more engagement (28\% more participants). Label-diff congruence influences how collaboration unfolds during review, supporting efficiency for experienced developers and visibility for newcomers. For projects with similar labeling conventions, monitoring alignment can help detect coordination friction and provide guidance when labels and code diverge.
The centralization of digital content creation and credentialing platforms has resulted in opaque monetization structures, monopolistic data silos, and a persistent absence of verifiable user sovereignty over intellectual contributions. This paper introduces Metaplay, a decentralized content marketplace architecture engineered to disintermediate the content creation and talent development lifecycle. Leveraging a modular blockchain framework, Metaplay utilizes Zero-Knowledge Rollups (zkEVM) for high-throughput, low-latency execution, and EIP-4844 blob-carrying transactions to minimize data availability costs. We introduce a privacy-preserving credentialing mechanism utilizing Soulbound Tokens (SBTs) and zk-SNARKs, enabling non-transferable, cryptographically verifiable proof of skill acquisition without compromising user privacy. Platform moderation employs a Decentralized Autonomous Organization with Identity-Gated Quadratic Voting to mitigate plutocratic governance capture. A dual-token incentive model (PLAY utility token and CRED reputation token) aligns creator economic incentives with verifiable content quality. Comparative benchmarks demonstrate transaction cost reductions exceeding 95% relative to Ethereum Layer-1 baselines.
Abstract Software development plays a central role in digital sustainability, yet developers’ role and engagement remains understudied. Here we analyse nearly a decade of developer discussions available on the code repository Github on Ethereum, a widely used open-source blockchain platform. Using topic modelling, with interpretation supported by large language models and a sustainability framework for software systems, we trace how economic, environmental, social, individual, and technical sustainability themes emerge and evolve over time. We find that sustainability awareness, particularly related to energy efficiency and cost, intensifies during key events such as the transition from proof-of-work to proof-of-stake consensus, which substantially reduced energy use. We identify influential contributors and thematic specialisation, providing a transferable framework for understanding sustainability in emerging developer communities. These findings highlight the role of developer discourse in shaping sustainable software ecosystems and integrating sustainability into open-source development.
Large language models have intensified a growing property-rights challenge in digital markets: protected works can be copied, retrieved, transformed, and recombined at low marginal cost, while ownership, licensing authority, attribution, and remuneration remain costly to verify. First, I introduce the Model Context Protocol (MCP) as an interoperability layer between AI agents and intellectual-property institutions. MCP does not define rights or settle disputes; it gives agents a standardized way to query registries, invoke licensing tools, execute payments, record usage, and preserve audit trails. Second, I develop a stylized transaction-cost model of agentic licensing and derive comparative statics for when lawful exchange expands. Lower search, verification, contracting, payment, and monitoring costs can move marginal uses from avoidance, substitution, or unauthorized use into licensed exchange, especially when rights records are reliable, license terms are standardized, and interface costs are large relative to the price of the license. Third, I explain how non-fungible tokens (NFTs) can complement MCP when they operate not as collectibles, but as machine-readable rights objects linked to work identifiers, ownership claims, license scope, payment rules, provenance records, audit obligations, and dispute forums. Music licensing is illustrative because rights are fragmented across compositions, recordings, labels, publishers, performers, territories, and use types. MCP and NFT-linked rights records can support ex ante licensing when paired with verified title, enforceable contracts, bounded delegation, human review, and off-chain legal remedies.
Decentralised communities increasingly need ways to generate, contest, and maintain shared knowledge without relying on central editorial authorities. Existing collaborative systems such as wikis support large-scale knowledge production, but typically depend on centralised moderation and governance, while blockchain consensus mechanisms are primarily designed for transactional state rather than the validation and revision of shared claims. This paper introduces Pensieve, a framework for decentralised epistemic governance in collaboratively governed knowledge bases. Pensieve combines structured submissions, contribution-weighted support, and stake-backed dispute escalation to govern the publication, revision, and contestation of knowledge entries over time. The paper makes three contributions. First, it presents Pensieve as a framework for decentralised epistemic governance designed specifically for collaboratively governed knowledge bases rather than for transactional ledgers alone. Second, it formalises a contribution-weighted model of item governance and dispute resolution, showing how reputational participation and economic stake may be combined to support knowledge revision that remains both dynamic and increasingly stable over time. Third, it examines the Pensieve instance on ECF.network as an early live implementation context through which the framework is being applied to the collaborative documentation and evaluation of Web3 projects. The case study provides descriptive evidence that the framework can support structured publication, community contribution, dispute handling, and AI-assisted but human-accountable input in practice, while also revealing unresolved challenges around referencing, sustained participation, postpublication revision, and long-term governance design. Pensieve is therefore offered not as a completed solution to decentralised knowledge governance, but as a research framework and live design experiment for coordinating collective knowledge production in human-AI systems.