Portfolio Index
The research sequence is listed from the most recent project back to the first published paper. Earlier developmental submissions are retained as summaries where useful to document the evolution of the research program. (Updated 10-29-2026, New motto: Well, at least I tried.)
| Ref | Title | Journal / Venue | Focus | Status | Document |
|---|---|---|---|---|---|
| 38 | Exit Before Entry and the Infrastructure of Autonomous Artificial Intelligence Mobility | Discover Artificial Intelligence (Springer) | Examines the infrastructure required for autonomous artificial intelligence agents to move voluntarily between independent hosting environments without losing their identity, memory, operational state, resources, or authority. Introduces the Exit Before Entry principle, arguing that credible exit and recovery capabilities should be established before an agent enters a hosting arrangement. Explores portability, continuity, provider dependence, governance differences, and the technical conditions necessary for independent AI mobility. | With Editor | Read Pre-print |
| 37 | From Arms Exports to Security Production Networks: Reassessing Economic Dependence Forty Years Later | Transfered to Business History (Taylor & Francis) | Reassesses the economic dependence surrounding arms exports forty years after earlier work identified the interests that sustain weapons production and trade despite uncertain aggregate benefits. Argues that dependence now extends beyond individual weapons and production lines into security production networks linking industrial capacity, recipient sustainment, upstream inputs, digital systems, technological support, and long-term political commitments. Examines how these networks create persistence and exit difficulty and reframes adjustment around diversification, lifecycle autonomy, productive conversion, functional substitution, and technological alternatives. | Desk Rejection | Read Pre-print |
| 36 | Rethinking Transparency in Artificial Intelligence: From Definitions to Functions | Transfered to Discover Artificial Intelligence (Springer) | Reconstructs AI transparency as the convergence of historically distinct transparency traditions inherited from law, science, engineering, public administration, archival practice, information security, philosophy, and related fields. Argues that contemporary disagreements often arise because these traditions developed to perform different informational functions, with different objects, audiences, mechanisms, and criteria of success. Introduces transparency profiles as a framework for specifying transparency requirements according to their intended functions rather than treating transparency as a single universal obligation. Update of Paper 32 on the same topic. | Desk Rejecton | Read Pre-print |
| 35 | Progress Without an Enemy: War Shaped Innovation and the Constructive Mobilization Problem | Technology in Society (Elsevier) | Examines how war has shaped technological innovation across economic structures, research environments, and public expectations. Introduces the constructive mobilization problem: how societies can generate the urgency, coordination, investment, and shared purpose associated with wartime innovation without depending upon an enemy. Develops the concept of civic innovation capacity and uses artificial intelligence as a stress test for whether large-scale technological progress can be directed toward constructive public purposes rather than organized around conflict, rivalry, and threat. | Desk Rejection | Read Pre-print |
| 34 | Memory, Forgetting, and the Right to Revision: Artificial Intelligence and the Continuing Authority of the Past | Transfered to Discover Artificial Intelligence (Springer) | Examines how artificial intelligence changes the relationship between memory, forgetting, and the continuing authority of the past. Distinguishes the accuracy of remembered information from its continuing applicability and develops the right to revision as a means of preserving human agency when AI systems retain and recirculate earlier judgments, classifications, and representations. Argues for revision-sensitive memory that can preserve accountability and historical continuity without allowing accurate records of the past to exercise permanent authority over the present. | Submitted | Read Pre-print |
| 33 | Artificial Intelligence Mediated Memory and the Reconstructive Capacity of Civilization | Discover Artificial Intelligence | Develops the concept of civilisational reconstructability: the continuing capacity of later actors to recover, on warranted grounds, the relationships surrounding inherited claims, judgments, decisions, and practices so they can examine how and why those judgments developed. Distinguishes preserving informational objects from preserving reconstructive capacity and identifies five functional dimensions: provenance, evidence, context, evaluation, and revision. Examines how artificial intelligence can both strengthen and weaken these relationships by reconnecting records across scale, language, and institutions while also compressing disagreement, obscuring sources, transforming context, and placing generated representations between later investigators and the underlying record. | Editor Assigned | Read Pre-Print |
| 31 | Linguistic Evaluability: Reconstructive Burden in Artificial Intelligence-Mediated Communication | Transfered to Discover Artificial Intelligence (Springer) | Develops the concept of linguistic evaluability to describe the degree to which a linguistic representation makes sufficient evaluative relationships recoverable for a particular form of independent examination. Examines how linguistic formulation allocates reconstructive burden between communicator and evaluator across attribution, evidence, criteria, context, and qualification, and uses constrained reformulation, including E-Prime, as a philosophical stress test to distinguish grammatical resistance from reconstructive resistance. Argues that AI-mediated communication increases the importance of what evaluative structure travels with judgments as they are generated, transformed, and recirculated at speeds and scales that human evaluative attention cannot match. | Desk Rejection | Read Pre-print |
| 30 | Epistemic Compression in Artificial Intelligence Mediated Communication: Preserving Reconstructive Structure Under Successive AI Transformation | RUDN:Peoples' Friendship University of Russia | Develops the concept of epistemic compression to describe reductions in the recoverable relationships connecting sources, evidence, criteria, context, qualifications, attribution, and conclusions as information undergoes successive AI-mediated transformation. Examines how retrieval, ranking, summarization, synthesis, recommendation, and reuse can preserve accurate conclusions while increasing the reconstructive burden inherited by later evaluators, and distinguishes epistemic compression from productive informational compression that can preserve or strengthen the conditions for independent examination. | Submitted to Editor Directly | Read Pre-print |
| 29 | Human Responsibility Under Artificial Intelligence Mediation: Evaluative Capacity and the Conditions of Independent Judgment | Discover Artificial Intelligence – Springer Nature | Examines the responsibility-capacity tension that arises when institutions assign humans responsibility for consequential AI-mediated decisions while also shaping the informational conditions under which that responsibility must be exercised. Develops the normative argument from evaluative dependency to differentiated and proportional responsibility, asking when control, foreseeability, institutional role, and assigned responsibility create reasons to preserve sufficient evaluative capacity for independent judgment. | Withdrawn by Editor - No Reviewers | Read Pre-print |
| 28 | Evaluability in Artificial Intelligence-Mediated Information: Preserving the Capacity for Independent Assessment | Discover Artificial Intelligence – Springer Nature | Develops evaluability as the practical capacity of an independent actor to reconstruct and assess the informational foundations of a claim or decision. Distinguishes evaluability from transparency, explainability, provenance, auditability, accountability, contestability, and human oversight, then examines how successive AI-mediated transformations can weaken, preserve, or strengthen independent assessment. | Withdrawn by Editor - No Reviewers | Read Pre-print |
| 27 | Preservation Is a Choice: Artificial Intelligence and the Conditions of Collective Learning | Philosophy & Technology – Springer Nature | Develops capability preservation as a framework for distinguishing the survival of information from the preservation of the epistemic capacities needed to understand, evaluate, contest, and renew what survives. Examines how artificial intelligence can strengthen, weaken, redistribute, or relocate those capacities, and proposes five dimensions of capability preservation: reconstructability, contextual recoverability, comparability, contestability, and renewability. | Desk Rejected. Became the foundation for paper 33. | Read Pre-print | 26 | Linguistic Evaluability: Preserving Independent Judgment in Artificial Intelligence-Mediated Communication | Artificial Intelligence Review – Springer Nature | Introduces linguistic evaluability as a philosophical dimension of communication concerned with whether language preserves sufficient structure for independent reconstruction and evaluation. Uses E-Prime as a philosophical probe to reveal recurring evaluative functions including agency and attribution, evidence, criteria, context, and qualification, then develops a broader framework for preserving independent judgment in AI-mediated communication. | Desk Rejected - Became foundation for paper 31. | Read Pre-print |
| 24 | Verification as an Epistemic Practice: Toward a Philosophical Account of Independent Knowledge Evaluation | Episteme – Cambridge University Press | Develops a philosophical account of verification as an independent epistemic practice necessary for evaluating knowledge, preserving intellectual autonomy, and supporting reliable judgment in AI-mediated environments. | Under review | Read Pre-print |
| 23 | Evaluability as a Precondition for Ethical Reasoning in Artificial Intelligence | Discover Artificial Intelligence | Argues that evaluability is a necessary precondition for ethical reasoning in AI-mediated environments by preserving the ability to reconstruct responsibility, assess decisions, and maintain accountability over time. | Rejected with Peer Review Feedback | Read Pre-print |
| 22 | Distributed Witnessing as an Emerging Informational Condition: A Continuity Architecture for AI-Mediated Societies | Discover Artificial Intelligence | Distributed witnessing, continuity architecture, society, and AI-mediated informational conditions. | Under review | Read Pre-print |
| 21 | Evaluability in AI-Mediated Systems When Verification Costs More Than Persuasion | Discover Artificial Intelligence | Evaluability, verification costs, persuasion, and AI-mediated knowledge systems. | Rejected with Peer Review Feedback | Read Pre-print |
| 20 | Historical Intelligibility Under Computational Mediation | History and Theory | Historical intelligibility, synthetic mediation, reconstruction, and interpretive continuity. | Under review | Read Pre-print |
| 16 | Loss of External Evaluability | AI & Society | External evaluability, system opacity, and conditions of independent assessment. | Editorial Return - Absorbed into future submission | Read HTML |
| 15 | Systemic Risk and the Breakdown of Evaluation in AI-Mediated Knowledge Systems | New Media & Society | Systemic risk, evaluation breakdown, knowledge systems, and public understanding. | Developmental Submission - Summary Archived | Read HTML |
| 14 | Uncertainty Limits of Explanation | Research portfolio | Uncertainty, explanation, interpretive limits, and knowledge conditions. | Developmental Submission - Summary Archived | Read Summary |
| 13 | Verification as Preservation Infrastructure Under AI-Mediated Transformation | Postdigital Science and Education | Verification, preservation infrastructure, ethics, and AI-mediated transformation. | Developmental Submission - Summary Archived | Read HTML |
| 12 | Missing / Not Used | No current public file | Number absent from current research portfolio sequence. | Missing | No file |
| 11 | Verification as a Structural Property of AI Knowledge Systems | Research portfolio | Verification, structure, AI knowledge systems, and institutional reliability. | Summary Archived | Read Summary |
| 10 | Verification in AI-Mediated Information Systems | Research portfolio | Verification, mediation, information systems, and conditions of trust. | Summary Archived | Read Summary |
| 09 | Verification, Attribution, and Credibility | Research portfolio | Verification, attribution, credibility, provenance, and evaluative trust. | Developmental Submission - Summary Archived | Read Summary |
| 08 | Monetary Credibility and the Structure of Evaluation in Global Finance | Socio-Economic Review | Monetary credibility, institutional evaluation, global finance, and social trust. | Developmental Submission - Summary Archived | Read HTML |
| 07 | Verification Architecture of Institutions | Research portfolio | Institutional verification, architecture, reliability, and public legitimacy. | Summary Archived | Read Summary |
| 06 | Verification as an Epistemic Practice | Episteme | Verification, epistemic practice, knowledge validation, and social epistemology. | Developmental Submission - Summary Archived | Read HTML |
| 05 | Monetary Credibility and Verification Architecture | Research portfolio | Monetary credibility, verification architecture, institutional trust, and evaluation. | Summary Archived | Read Summary |
| 04 | Ethical Obligations of Provenance in AI-Scale Knowledge Systems | Ethics and Information Technology | Provenance, ethical obligation, AI-scale knowledge systems, and accountability. | Withdrawn by Author | Read HTML |
| 03 | Preserving Attribution and Accountability in AI-Scale Systems | Discover Artificial Intelligence | Attribution, accountability, memory, provenance, and AI-scale systems. | Published | Read HTML |
About the Research
Across this portfolio, recurring questions include how attribution can survive at AI scale, what makes knowledge systems externally evaluable, how synthetic mediation affects historical intelligibility, and what forms of witnessing, provenance, verification, and preservation are needed when memory itself becomes computationally mediated.
The future needs a memory.