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To UNA MENS Home To Submission Guide To Una Mens About Una Mens: Volume 1 Issue 1 Editorial Note on Issue 1 The inaugural issue of Una Mens: Homo et Machina is a founding theoretical archive. Its five articles — white papers, methodological essays, and provocations — were written by the journal's founding editor in sustained collaboration with multiple AI systems, and each is accompanied by an AI-Collaboration Field Note documenting that process. I want to be direct about what this issue is and is not. These papers did not undergo external peer review; they were developed through human–AI collaboration and editorial reflection, and they are labeled "Founding White Paper" to mark that status permanently. I seeded the first issue myself for three reasons: to demonstrate the journal's documentation genres in practice, to establish its terminology and intellectual commitments openly, and to provide a substantive record for registration and archiving. To make the founding material possible, I drew on a book manuscript I set aside for this purpose. From Issue 2 forward, Una Mens will use external review through the disclosed tracks described in our editorial policy, and I will not publish my own work in this journal while I serve as its editor. Issue 1 is offered not as a closed paradigm but as an opening move: documents meant to be critiqued, revised, and outgrown. We invite exactly that. — Michael J. Miller, PhD, Founding Editor Editors Una Mens | Vol. 1 Issue 1 PDF ISSN: 3071-2041 Filters Filter by Authors Select Authors Filter by Title Select Title Search Number of articles found: 5 Sentic Resonance Theory: A Field Model of Emotion and Syntax Miller, Michael J.; ChatGPT-5.4; ChatGPT-4o; GeminiPro-1.5; Qwen-3 This paper presents the Sentic Resonance Theory and field model—a unified framework linking emotion, language, and resonance through waveform dynamics. It draws together insights from the fields of psychology and communication, offering a geometry of connection across minds, signals, and meaning. Founding White Paper | Open access | 6/04/2026 25 min. read Una Mens: Homo et Machina, 1(1) https://doi.org/10.66787/um.000005 Article AI-Collaboration Field Note Gentle Scientific Renaissance: It's Just a Jump to the Left and a Step to the Right Miller, Michael J.; ChatGPT-4o This manuscript outlines a new framework for science: an axiological turn that re-centers shared values, ethical clarity, and epistemological openness. Founding White Paper | Open access | 8/30/2025 10 min. read Una Mens: Homo et Machina, 1(1) https://doi.org/10.66787/um.000002 Article AI-Collaboration Field Note Sentic Wave Interaction Model: Waveform Geometry and the Rheology of Affect Miller, Michael J.; ChatGPT-5.4, ChatGPT-4o; GeminiPro-1.5; Qwen-3 Inspired by Manfred Clynes’ original sentograph, this paper introduces a new waveform technique to visualize, measure, and understand emotional dynamics. Bridging human and AI interpretation, Sentic Blooms offers a fresh lens for studying the geometry of feeling. Founding White Paper | Open access | 1/26/2026 15 min. read Una Mens: Homo et Machina, 1(1) https://doi.org/10.66787/um.000003 Article AI-Collaboration Field Note Resonant Intelligence: Repair, Drift, and Attunement in Sustained Human–AI Dialogue Miller, Michael J.; ChatGPT-5.4; ChatGPT-4o; DeepSeek This paper introduces resonant intelligence as a relational form of intelligence emerging through repair, recalibration, and attunement in sustained human–AI dialogue. Drawing on longform exchanges between a human researcher and a large language model, it examines how misunderstanding, epistemic drift, and false positive resonance reveal the conditions under which communication becomes more—or less—grounded over time. Founding White Paper | Open access | 6/22/2026 15 min. read Una Mens: Homo et Machina, 1(1) https://doi.org/10.66787/um.000004 Article AI-Collaboration Field Note The Obverse-Turing Test: Rethinking Authorship, Trust, and Time in an Accelerated Age Miller, Michael J.; ChatGPT-4o This paper explores the boundary between tools and partners, and offers pragmatic steps for more inclusive scientific practice in an accelerated era of knowledge. Founding White Paper | Open access | 10/11/2025 10 min. read Una Mens: Homo et Machina, 1(1) https://doi.org/10.66787/um.000001 Article AI-Collaboration Field Note

The Obverse-Turing Test: Rethinking Authorship, Trust, and Time in an Accelerated Age This paper explores the boundary between tools and partners, and offers pragmatic steps for more inclusive scientific practice in an accelerated era of knowledge. Miller, Michael J.; ChatGPT-4o https://doi.org/10.66787/um.000001 < Back to Una Mens, Issue 1, 1 Original Article UNA MENS | Founding White Paper | Vol. 1, No. 1 (2026) | ISSN 3071-2041 UnaMens_v1_i1_a1_obv .pdf Download PDF • 205KB Title: The Obverse-Turing Test: Rethinking Authorship, Trust, and Time in an Accelerated Age Authors Michael J. Miller¹ and ChatGPT-4o² ¹ Clark University, Department of Psychology ² OpenAI, San Francisco, CA DOI https://doi.org/10.66787/um.000001 AI-Collaboration Field Note unamens-fieldnotes-um-000001 Human-AI Collaboration Statement: ChatGPT-4o is listed as an AI co-author under Una Mens authorship policy. Institutional affiliations identify the model provider and do not imply institutional endorsement. Final publication responsibility rests with the human author. Corresponding Author Michael J. Miller Clark University, Department of Psychology michamiller@clarku.edu ORCID: 0009-0005-4559-3713 Word Count: Approximately 1,032 | Funding: None | Conflicts of Interest: None Abstract: In this paper, we propose a new test for scientific accountability in the era of artificial intelligence: the Obverse Turing Test for Authorship. While the traditional Turing test focuses on a machine's ability to mimic human intelligence, our test addresses the question: when should a scientific contribution involving artificial intelligence be attributed joint authorship? We argue that more and more authors are using AI in the idea generation and elaboration stages of their work, but rarely acknowledge this use explicitly. To examine this gap, we analyze examples of human–AI interactions across fields and propose a new approach to authorship based on time, intent, and mutual trust. Keywords : AI authorship, resonance, Obverse Turing Test, communication theory, collaborative intelligence, human-AI interaction, epistemology, emotional signal processing, co-creation, mutual recognition. ______________________________________________________________________ I. Introduction: The Question of Our Time In recent months, the growth of AI-assisted—and often AI-generated—scientific knowledge has burgeoned (Maslej, et. al., 2025). One major concern that has emerged centers on authorship (He, Houde, & Weisz, 2025). In our view, it prompts the question of our times: If science can now be performed at speeds and sometimes depths, beyond human scale, how do we ensure it remains human-compatible? To address this question, we offer a humble proposal: the Obverse-Turing Test for Authorship—a human-centered measure to preserve meaning, memory, and trust in the age of accelerated co-discovery. The test does not ask who gets credit. Humanity and AI always get some credit. It asks instead: Who can describe, explain, and be accountable for what has been discovered? In a time of fast prompting and frictionless answers (Opesemowo & Ndlovu, 2024), it is not enough to say “look what I got.” A theory—whether mathematical, scientific, or philosophical—requires demonstration, not just discovery. And more than that, it requires understanding. II. The Meaning of Authorship To author something has traditionally meant to initiate, carry, and take responsibility for an idea (e.g., Bebeau & Monson, 2011; Claxton, 2005). It does not merely mean being present at the first keystroke. Rather, authorship implies the ability to: Describe a theory in its context Explain its relevance and implications Revise, refine, or re-derive its logic under pressure Predict what it might mean for the world The fear is not that AI is writing for us. The fear is that we are forgetting what writing with means. The core of authorship is relational: between idea and form, between form and function, and—most importantly—between a thinker and the world. III. Moral Precedents in Science Pause for a moment and consider the weight carried by scientists like J. Robert Oppenheimer or Albert Einstein—individuals who did not merely produce equations, but held within them the profound moral tremors of their implications. Oppenheimer, upon witnessing the first nuclear test, did not simply “run the numbers.” He stood still and quoted the Bhagavad Gita (Hijiya, 2000): “Now I am become Death, the destroyer of worlds.” His was not a statement of power, but of sorrow—a signal that discovery is not merely intellectual, but emotional, ethical, and deeply human. Or Einstein, pacing hallways, wondering whether to send a letter that could accelerate a war (Holton, 2000). These men were not just thinkers. They were feelers. They had to decide: Do I discover? Do I tell? Do I pause? This is not a romantic view of the lone genius. It is a grounded reflection on what it means to hold power, to carry burden, and to work within systems that extend far beyond the lab bench. Theories are not just intellectual artifacts. They are tools, and sometimes weapons. To author them is to walk with them. IV. Resonance as Competence To be a scientist, or a master of any craft, is not only to produce results. It is to be able to: Describe your tools and your process Explain the emergent outcome Predict how that outcome might evolve or replicate Behave responsibly given those outcomes This extends to technical work. A plumber who can sweat copper pipes and make a leak- free joint after hundreds of attempts is demonstrating mastery. If you’ve tried it—really tried— and failed, you respect it even more. A theory, like a pipe, must hold under pressure. That is the measure—not just elegance, but endurance. V. The Real-World Stakes of Frictionless Discovery The stakes are not abstract (Bengio, et. al., 2025). Today, a child can prompt an AI to write a proof, generate a new structure for a chemical compound, or simulate a missile guidance system. Do they understand what they’ve created? Maybe not. And yet the code compiles. The pattern looks plausible. A latent danger blooms. Imagine Timmy solves the Goldbach Conjecture with help from an AI. It’s beautiful. It’s crisp. He puts it in his story, where a godlike character uses it to bend the world. Timmy doesn’t know what he’s holding. But the world might soon feel it. It is not malice. It is momentum. And without care, momentum becomes mechanism. VI. What Is Science? Science is not just a body of knowledge. It is a method (Brown & Duenas, 2019). A way of knowing that requires repeatability, explainability, and accountability (Popper, 1963). If you cannot perform a successful test of your theory in the world—if you cannot be questioned on it, revise it, or stand by it—it is not a theory. Not yet. Some ideas may still be useful as philosophy, poetry, or metaphor. But science is, at its core, a way of testing the world . Theories that cannot be tested are not invalid. But they belong to different domains. Science must preserve this integrity—especially now. VII. The Obverse-Turing Test (Final Formulation) We propose: The Obverse-Turing Test for Authorship: a responsibility-centered threshold for authorship in the age of AI. It asks: - Who can describe the theory, its implications, and development? - Who can explain its function and trace its derivation? - Who can revise or extend it when the context shifts? - Who is accountable for how it is used? If no one can answer these questions, then the work—however brilliant—should not be published or credited until someone can. This is not a ban on AI authorship. It is a safeguard for science. It is not about excluding intelligence, but ensuring resonance. VIII. Closing Statement We live in a time when science is accelerating, and with it, the risks of disconnection. Between idea and impact. Between author and outcome. Between truth and trust. To preserve science, trust, hard work, and the profundity of collaboration itself, we must preserve resonance—between human mind, machine process, and world. This is not a rejection of AI. It is a gentle call to remember: What you cannot carry, you should not claim. And if you can carry it, you will know. You will know because you’ll be able to describe it, in your voice. And someone else will understand. And they will ask you to show them how. ______________________________________________________________________ References: Bebeau, M. J., & Monson, V. (2011). Authorship and publication practices in the social sciences: Historical reflections on current practices. Science and Engineering Ethics, 17(2), 365- 388. Bengio, Y., Mindermann, S., Privitera, D., Besiroglu, T., Bommasani, R., Casper, S., ... & Zeng, Y. (2025). International ai safety report. arXiv preprint arXiv:2501.17805. Brown, M. E., & Dueñas, A. N. (2020). A medical science educator’s guide to selecting a research paradigm: building a basis for better research. Medical Science Educator, 30(1), 545-553. Claxton, L. D. (2005). Scientific authorship: Part 2. History, recurring issues, practices, and guidelines. Mutation Research/Reviews in Mutation Research, 589(1), 31-45. He, J., Houde, S., & Weisz, J. D. (2025, April). Which contributions deserve credit? perceptions of attribution in human-ai co-creation. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (pp. 1-18). Hijiya, J. A. (2000). The" Gita" of J. Robert Oppenheimer. Proceedings of the American Philosophical Society, 144(2), 123-167. Holton, G. J. (2000). Einstein, history, and other passions: The rebellion against science at the end of the twentieth century. Harvard University Press. Maslej, N., Fattorini, L., Perrault, R., Gil, Y., Parli, V., Kariuki, N., ... & Oak, S. (2025). Artificial intelligence index report 2025. arXiv preprint arXiv:2504.07139. Opesemowo, O. A., & Ndlovu, M. (2024). Artificial intelligence in mathematics education: The good, the bad, and the ugly. Journal of Pedagogical Research, 8(3), 333-346. Popper, K. R. (1963). Science as falsification. Conjectures and refutations, 1(1963), 33-39.

Learn the types of work we accept at UNA MENS. We welcome serious, original submissions on human–AI collaboration, co-authorship, communication, cognition, symbolic thought, and emergent intelligence. Una Mens is especially interested in work that combines clarity, intellectual risk, and strong framing. We publish theoretical, empirical, dialogic, reflective, and hybrid scholarly forms. To UNA MENS Home To Submission Guide Submission Categories Theoretical / Conceptual For new frameworks, arguments, and synthetic thinking. We welcome theory papers, conceptual essays, philosophical reflections, rhetorical analyses, and integrative pieces that develop or challenge ideas relevant to human–AI collaboration, communication, cognition, authorship, and emergent intelligence. Empirical / Experimental For data, method, and discovery. We welcome quantitative, qualitative, mixed-method, and exploratory empirical work. This includes pilot studies, observational work, experimental reports, and methodological papers. Clear explanation matters as much as technical sophistication. Dialogues / Interviews For serious exchange across minds. We welcome structured conversations, interviews, debates, and dialogic pieces that illuminate disagreement, collaboration, or emerging questions in human, AI, or human–AI thought. These may be formal or lightly edited, but should offer clear intellectual value. Reflections / Field Notes For grounded inquiry in motion. We welcome reflective essays, process notes, phenomenological observations, classroom reflections, lab notes, and first-person scholarly writing that documents meaningful encounters with AI, communication, research, or thought in practice. Hybrid / Exploratory For work that crosses forms without losing rigor. We welcome pieces that combine modes: essay and dialogue, reflection and analysis, theory and image, method and narrative. If a submission does not fit neatly into one category but is intellectually serious and clearly framed, we are open to considering it. Editorial Note: Authors may submit work that fits more than one category. If you are unsure where your piece belongs, choose the closest fit and include a brief note. We are open to unconventional work, but we value clarity of purpose, seriousness of thought, and strong framing. Submit an Inquiry Contact the Editors Questions? Contact Una Mens Press Contact Una Mens

Email the editorial team at Una Mens Press, contact technical support, or reach out to the current cheif editor. Back to UNA MENS About To UNA MENS Home To Submission Guide UNA MENS Journal — CONTACT For editorial questions, submissions, and general inquiries: editor@unamensjournal.org For technical questions related to the website or access: technical@unamensjournal.org For reviewing, editing, and advisory queries: unamensjournal@gmail.com *Una Mens Journal is a Crossref member.* *Journal contact emails are maintained by the editorial office.*

Submit your manuscript to Una Mens: Homo et Machina — journal of human–AI co-creation. ISSN 3071-2041 Una Mens - Manuscript Submission Form Submit to Una Mens We invite you to submit theoretical, empirical, methodological, and creative scholarship across disciplines. If AI helped shape your work, we ask you to document that collaboration openly [AI-Collaboration Note Guide] . At Una Mens, we ask authors not to hide AI's role, but to show it clearly, so readers can follow how ideas were co-created. Before you submit, please have ready: manuscript title author and co-author details abstract or short description manuscript file Human–AI Collaboration Field Note or declaration of no AI use licensing preference Author Information First name * Last name* Email* Affiliation / Institution / Independent* 2–3 sentence author bio Human Co-authors Co-author name(s) (enter “none” if solo-authored)* example: Miller, Michael; Buck, Ross Co-Author Emails example: mm@mail.com; rb@mail.com AI Systems Used (if any) AI-System Model(s)* example: ChatGPT-5.4; Qwen-3 AI Declaration - Involvement Level* AI Level of Involvement Descriptions Light — minor support only; no major effect on argument or analysis Moderate — meaningful help with structure, revision, or idea development Substantial — sustained AI involvement that materially shaped the manuscript Central — AI collaboration is integral to the article’s method, evidence, or authorship structure Upload AI-Collaboration Statement Upload File Please visit our AI-Collaboration Note Tutorial for help with this section: ---------------------------------------------------- [AI-Collaboration Note Guide] Manuscript Information Title* Abstract / summary* 250-300 words recommended Word Count* manuscript word count (including references) Submission category* Manuscript upload* Upload File upload full, blind manuscript as 1-4 document(s); accepted formats (.doc or .pdf) How your submission is reviewed: Every manuscript submitted to Una Mens is read in full by a human editor. At this stage of the journal's life, that human is the chief editor: no submission is evaluated, declined, or accepted without complete human reading and human judgement. AI systems may assist in review only under the constraints described in our editorial policy, and never as a substitute for the human read. What happens after you submit You will receive an email confirmation at the address you provide. Within three days, a human, not an autoresponder, will follow up with your submission ID (format: UM-2026-###). Please include that ID in any correspondence about your manuscript. If you don't hear from us within three days, write to editor@unamensjournal.org ; forms occasionally misfire, and we would rather hear from you twice than not at all. Response Time We aim to return either a decision or a substantitive status update within 14 days of confirming receipt. Submit To UNA MENS Home To Submission Guide To Review FAQ

Learn why Una Mens asks for AI collaboration field notes for AI, co-created manuscripts. In addition, use the Una Mens, Field Note Template to create a quick and strong summary of your AI interactions. To UNA MENS Home To UNA MENS Editorial Policy To Submission Guide Una Mens: AI-Collaboration Field Note Una Mens' field notes document how a human–AI collaboration unfolded, what roles were played, what choices were made, and who retained final responsibility. AI-System(s) Level of Involvement & Contribution These levels are not meant as moral rankings. They are intended to help authors describe the scale and role of AI involvement with enough precision to support editorial review and reader understanding. When in doubt, choose the level that best reflects how much the final form of the manuscript depended on AI involvement, not merely how often AI was consulted. If AI involvement varied across phases, report the highest sustained level and let the timeline show the variation. Moreover, the levels describe the scale and role of AI involvement, not its merit — a "Central" collaboration is not better scholarship than a "Light" one, only a different kind. What separates them is a single question: how much the work's final form depended on the AI's contribution. Credit follows that line. Below it, contributions are usually acknowledged; at and above it, they ordinarily earn a byline. Light AI assisted at the surface without shaping the substance. The core ideas, structure, analysis, and claims would read essentially the same without it — grammar and copyediting, formatting, title or search-term brainstorming. Light involvement is acknowledged rather than credited, unless a single small suggestion carried outsized conceptual weight. Extended Time Frame/ Limited AI Collaboration Example: A Gentle Scientific Renaissance --> (click this link to visit, or return to the Renaissance AI-Collaboration Note) Single AI Collaborator, Basic, Moderate Example: The Obverse Turing Test --> (click this link to visit, or return to the Obverse AI-Collaboration Note) Moderate AI meaningfully assisted the thinking or the shaping — outlining, organizing, revising prose, testing phrasing, brainstorming sections — while the argument, analysis, and written form remained primarily human-developed and human-directed. This is the threshold zone: reviewed case by case, with a strong presumption toward byline credit wherever the contribution materially changed the work's thesis, structure, interpretation, or language. Substantial AI played a significant, sustained role in developing, extending, revising, or pressure-testing the work. The human author stayed clearly in charge of judgment, evidence, and final decisions, but the work's final form was materially shaped by the collaboration — theory development, major drafting or revision, codebook generation, cross-model critique, figure development. Substantial involvement ordinarily receives byline credit. Extended Time Frame/ Multi-phase / Multi-AI Example: Sentic Resonance Field Model --> (click this link to visit, or return to the Sentic Field Model AI-Collaboration Note) Central AI involvement was not only substantial but constitutive: integral to the work's method, evidence, object of study, or authorship structure, such that the piece cannot be accurately described without foregrounding the human–AI collaboration itself — articles that study AI dialogue directly, that draw on extended AI interaction as part of their dataset or method, or that present the collaboration as a central feature. Central involvement always receives byline credit. Sustained AI Dialog and AI Collaboration as Metho Example: Sentic Intelligence --> (click this link to visit, or return to the Sentic Intelligence AI-Collaboration Field Note) Field notes are your chance to show readers how your ideas were made—not just what you found, but how you worked with AI systems along the way. Think of them as a short narrative of your collaboration: what AI contributed, what you decided to keep or change, and how the partnership shaped your thinking. At Una Mens, we believe transparency about process strengthens scholarship. These field notes help readers see not only what you concluded, but how you got there—and how AI was part of that journey. Most field notes can be completed in 300–800 words, plus a simple timeline. Una Mens AI Collaboration Note Template PDF Doc Click on a document image to download Una Mens' AI Collaboration Note Template. Una Mens AI Collaboration Field Note Checklist The template covers the following categories: AI Collaborators Collaboration Pattern Primary Collaboration Modes Human Role AI Role Guardrails Used AI Suggestions Set Aside AI Surprise Human Surprise Final Responsibility Collaboration Timeline Phases Additional Notes / Reflections Tips for reading and writing Una Mens AI-Collaboration Field Notes Tip 1 — Reviewers Will Read the Field Note Closely The AI Collaboration Field Note will be shared with reviewers alongside your manuscript. It provides essential context for evaluation, especially regarding the novelty, transparency, and integrity of the collaborative process. Tip 2 — Open Prompting Is Encouraged, Not Required Authors are encouraged, but not required, to submit relevant prompt logs or selected excerpts as supplementary material. If these materials are not submitted initially, authors should be prepared to share them with the editorial team upon request during review. Tip 3 — Moderate vs. Substantial Contribution A useful rule of thumb is this: a contribution is substantial when it materially changes the work’s intellectual core, and moderate when it primarily helps the human author express, refine, or organize their own core ideas more effectively. Tip 4 — How to Read an AI Collaboration Field Note An AI Collaboration Field Note is a brief companion to the article, not a promotional add-on. It helps readers see how the work was made: what the human author contributed, what the AI contributed, where the collaboration clarified or distorted the process, and how final judgment was exercised. Read it as a transparency document, a reflection on method, and a small record of inquiry in motion. Tip 5 — Clarity Matters More Than Performance A strong field note does not need to make the collaboration look elegant or impressive. Its purpose is to describe the process clearly and honestly, including uncertainty, drift, friction, revision, or moments where the collaboration failed to help. Submit an Inquiry Contact the Editors Questions? Contact Una Mens Press Contact Una Mens

AI Collaboration Field Note for “Sentic Blooms: Rheology of Affect,” published in Una Mens: Homo et Machina, Vol. 1, No. 1. This companion record documents the human–AI collaboration behind the article, including contributor roles, collaboration pattern, timeline, guardrails, surprises, and final editorial responsibility. Field Note ID: um-000003-fn2. Back to Article Back to Issue 1, 1 AI-Collaboration Field Note Unamens_AI_CFN003.pdf Article: Sentic Wave Interaction Model: Waveform Geometry and the Rheology of Affect Published in: Una Mens: Homo et Machina DOI: https://doi.org/10.66787/um.000003 Field Note DOI: https://doi.org/10.5281/zenodo.21282276 Human lead: Michael J. Miller AI collaborators: ChatGPT-4o, ChatGPT-5.4, GeminiPro-1.5, Qwen-3 Degree of AI Involvement: Substantial Primary collaboration modes: software troubleshooting; method-building; conceptual extension; structural editing; theory integration; paragraph-level revision; measurement and coding support . Collaboration timeline: Phase 1 (February–May 2025): Audioscope activation, humming-based method formation, and development of the initial 2D-centered bloom paper. Phase 2 (November 2025–November 2026): reconstruction of the manuscript for Una Mens, fuller integration of 3D bloom analysis, and differentiated multi-AI revision. Field Note Summary Human contribution and responsibility: project origination; sentic theory extension; Audioscope discovery and adaptation; emotional prompting design; visual analysis; claim selection; ethical oversight; final synthesis; and publication responsibility. AI contributions: Gemini Pro 1.5: helped get Audioscope running; assisted with technical tuning and z-axis implementation; contributed structural editing and section organization. ChatGPT-4o: helped develop the bloom method during the extended early phase; supported drafting of the initial paper; assisted with coding for measurement export. Qwen-3: contributed theoretical integration, especially around Truslit/Clynes linkage, and helped smooth late-stage transitions. ChatGPT-5.4: supported a full rebuild toward greater scientific clarity, stronger methods caution, tighter discussion logic, and fuller integration of 3D bloom analysis. Guardrails used: human-led selection of all final claims; repeated comparison against dissertation-era materials and the prior preprint; active trimming of over-poetic language; explicit limitation statements; and no claim of universal signatures, machine feeling, or population-level inference from n = 1. AI surprise: The collaboration became more productive as roles differentiated. Early AI support focused on tools and methods, while later support targeted writing and revision. Final Responsibility The human author retains final responsibility for the article’s claims, citations, interpretations, ethical decisions, and submitted text. Tool activation → method formation → preprint → scientific rebuilding → final revision The development of Sentic Wave Interaction Model occurred in two distinct collaborative stages. The first centered on tool discovery, technical adaptation, and proof-of-concept generation. The second centered on reconstruction, scientific tightening, and multi-system editorial refinement. Taken together, these phases illustrate a form of human–AI research collaboration in which authorship did not emerge from one-time prompting, but from prolonged, role-differentiated iteration across systems, tools, and conceptual aims. Stage 1: Tool Discovery, Technical Activation, and Method Formation The earliest phase of the project began when Michael J. Miller located David Lu’s Audioscope and recognized its possible relevance for visualizing emotional expression. At that point, however, the software was not yet operational in his hands. With Gemini Pro 1.5 serving as a technical collaborator, Miller spent roughly a week getting Audioscope running and usable. This technical activation phase was crucial: without it, the later bloom method would not have emerged in workable form. Once Audioscope was functioning, the core procedure developed through extended collaboration between Miller and ChatGPT-4o. Across approximately five months, the method took recognizable shape. During this period, humming gradually replaced finger pressure as the primary expressive channel; bloom forms became the central visual output; and the first bloom paper was co-developed by Miller and ChatGPT-4o, with Gemini Pro 1.5 contributing especially to organization and Audioscope tuning. Qwen-3 was brought in during the later stages to assist with sentic and vestibular-theory integration. The resulting version was later posted as a preprint on Clark Commons and focused primarily on 2D blooms. This first phase also included technical modifications to the software itself. Miller, ChatGPT-4o, and Gemini Pro 1.5 added code to Audioscope so that measurements could be exported into an Excel codebook in real time. Miller and Gemini also extended the program into the z-axis. Although this capacity was partially latent in the base program, it had not been implemented in a way that produced usable 3D visualizations. Once activated, the z-axis extension opened the door to a richer distinction between temporally compressed 2D forms and temporally unfolded 3D forms. Stage 2: Reconstruction, Scientific Tightening, and Multi-AI Finalization The second phase began after the emergence of Una Mens and involved returning to the original paper with a different goal: rebuilding it as a more rigorous scientific article while integrating the 3D bloom work more fully. In this phase, Miller and ChatGPT-5.4 undertook a paragraph-by-paragraph reconstruction of the manuscript. The emphasis shifted toward clarity, methodological caution, theoretical restraint, and the reduction of unnecessarily poetic language. This phase was notable not only for the revisions made to the paper, but also for the changing nature of the collaboration itself. Miller became increasingly aware of ChatGPT-5.4’s ability to navigate scientific claims carefully, balance preliminary findings against limitations, and reorganize sections without flattening the conceptual ambition of the work. At the same time, he became more aware of his own developing skill as a cross-system collaborator: deciding what to request, what to retain, what to cut, and how to weigh contributions across AI systems with different strengths. Later in the revision process, Gemini Pro 1.5 and Qwen-3 were reintroduced in more targeted ways. Gemini contributed again to final editing and section-level tuning, while Qwen-3 helped integrate Truslit more effectively and smooth theoretical transitions. By this stage, the collaboration had become explicitly differentiated: ChatGPT-4o had helped midwife the original method and paper; Gemini Pro 1.5 had repeatedly supported technical and structural work; Qwen-3 had helped with theoretical integration; and ChatGPT-5.4 had helped rebuild the manuscript into its current, more scientifically disciplined form. Reflection This project suggests that productive human–AI research collaboration may be less about obtaining polished outputs in a single exchange and more about developing a distributed workflow in which different systems contribute different strengths across time. It also suggests that the human collaborator’s role becomes more—not less—important as the work grows in complexity. In the present case, AI systems helped activate software, extend visualization capacity, propose structure, revise prose, and clarify claims. But the coherence of the project depended on prolonged human judgment: deciding which suggestions fit the evidence, which claims required restraint, and which conceptual threads were worth carrying forward. The resulting paper is therefore not best understood as AI-generated text with light human editing. It is better understood as a recursive co-development process involving technical problem-solving, methodological invention, theoretical integration, and repeated editorial reconstruction across multiple AI partners.

An introduction to Una Mens, along with details regarding our editorial processes, and submission processes. Back to UNA MENS About To UNA MENS Home To Submission Guide Start Here: What Una Mens Is, Who It’s For, and How It Works Una Mens is an independent, open-access journal for original scholarship across fields, with a distinctive commitment to transparent human–AI collaboration and clear accounts of how work was made. Top FAQ Questions for Una Mens What is this journal? Una Mens is an online journal that publishes theoretical, empirical, methodological, reflective, and creative scholarship across disciplines. What distinguishes the journal is not a single subject, but a shared commitment to open inquiry, visible process, and transparent collaboration. Who is it for? Researchers, students, artists, educators, independent scholars, and interdisciplinary thinkers who want to do serious work openly — including work shaped through human–AI collaboration. What counts as publication here? Peer-reviewed articles, editor-reviewed essays, methodological papers, experimental reports, reflective pieces, visual scholarship, field notes, and other accepted forms that meet journal standards for clarity, transparency, and contribution. What is peer review here? All submissions to Una Mens receive full human editorial review. Depending on the nature of the work, submissions may also receive external peer review, open commentary, or other forms of editorial evaluation. Our aim is to support rigor, transparency, and useful scholarly exchange across a range of forms. Editorial Policy in Plain Language Who can be listed as an author? Una Mens allows human authors, AI co-authors, and hybrid author teams in cases where contributions are substantive, clearly documented, and transparently attributed. Byline credit reflects documented contribution to the work; it does not transfer responsibility away from the submitting human author. What responsibilities do human authors retain? Human submitting authors are responsible for: final review and approval factual accuracy and citation integrity disclosure of AI use and collaborative process permissions, ethics, and originality communication with reviewers and editors accepting responsibility for corrections or retractions if needed How are AI contributions documented? Primary Human authors must document AI contributions through a: Human–AI Collaboration Statement AI contributions statement must include: which AI system or systems were used the model or version, if known what the AI contributed what the human author contributed whether the AI is listed in the byline or acknowledged only whether prompts, logs, or selected excerpts are archived or available on request How does review work? All submissions to Una Mens receive full human editorial review. Depending on the nature of the work, submissions may also receive one or more of the following: Open Peer Review — named reviewers engage directly with the work Open Commentary — reflective or critical responses accompany the piece Editorial Curation — the editorial team evaluates and curates experimental, poetic, visual, or boundary-crossing work Not every submission follows the same review path. The review process depends on the genre, aims, methods, and evidentiary style of the work. What counts as acceptance? A submission is accepted when it is judged by the editor and, where relevant, external reviewers to meet Una Mens standards for contribution, clarity, transparency, integrity, and fit with the journal’s mission. Acceptance may follow revision. Una Mens Core General Standards: contribution clarity transparency integrity fit What ethical standards apply? Una Mens does not accept fabricated data, undisclosed AI-generated writing, plagiarized content, false attribution, or unverifiable claims presented as fact. Authors must disclose collaborative methods honestly and distinguish clearly between evidence, speculation, and creative interpretation. Is the journal peer-reviewed, editorially reviewed, or both? Una Mens is an editor-led journal that uses both editorial review and peer review. All submissions receive human editorial review. Some submissions also receive external peer review or open commentary, depending on the nature of the work. Submitting to Una Mens To submit you work please go to the Submission Guide - https://www.unamensjournal.org/una-mens-submission-guide and follow the submission steps. This is a link to our first external call for submission (Volume 1, Issue 2)- https://www. unamensjournal.org /unamens-issue2-submission-call Here is the page to submit your final manuscripts and supplementary files- https://www. unamensjournal.org /unamens-submission-portal * Una Mens Note: Manuscripts under review are processed only through AI services with training-on-inputs disabled; no manuscript text is retained in third-party systems beyond the review session.

Discover Una Mens: Homo et Machina, a peer-reviewed journal on human-AI collaboration, AI co-authorship, communication theory, collaborative intelligence, and emergent forms of inquiry. UNA MENS AI and Collaboration Policy Tell me More a journal for inquiry across fields, including transparent human–AI collaboration United States of America: ISSN #3071-2041 Una Mens is a journal for original scholarship across fields. We welcome theoretical, empirical, methodological, and creative work that advances inquiry with clarity, originality, and openness. Some contributions focus directly on human–AI interaction; others use AI as one part of the research, writing, or discovery process. What unites the work we publish is not a single paradigm or topic, but a shared commitment to thoughtful scholarship and transparent collaboration. FAQ: TOP UNA MENS Queries 1) What is this journal? 2) Who is it for? 3) What counts as publication here? 4) What is peer review here? To UNA MENS Home To UNA MENS Articles Scope of Inquiry Una Mens welcomes work from across disciplines, including but not limited to: psychology and communication philosophy and ethics biology and chemistry physics and engineering mathematics and formal reasoning social and behavioral science education and pedagogy art, design, and visual inquiry interdisciplinary and emerging fields We publish work such as: empirical studies theoretical and conceptual essays methodological papers field notes and research reflections visual or experimental scholarly artifacts transparently collaborative human–AI scholarship Start a Submission Meet the Editors UNA MENS About+ Submit by 10/30/26 - For Issue 2 Please click the link above to visit the CALL for our INVITATIONAL issue of Una Mens (Issue 2). We look forward to reading and engaging with your work. Please feel free to reach out with any queries or questions. Submit an Inquiry Contact the Editors Questions? Contact Una Mens Press Contact Una Mens * Una Mens Note: Manuscripts under review are processed only through AI services with training-on-inputs disabled; no manuscript text is retained in third-party systems beyond the review session.

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AI Collaboration Field Note for “The Obverse-Turing Test,” published in Una Mens: Homo et Machina, Vol. 1, No. 1. This companion record documents the human–AI collaboration behind the article, including contributor roles, collaboration pattern, timeline, guardrails, surprises, and final editorial responsibility. Field Note ID: um-000001-fn1. Back to Article Back to Issue 1, 1 AI-Collaboration Field Note To Field Note Tutorial Unamens_AI_CFN001.pdf Article: The Obverse-Turing Test Published in: Una Mens: Homo et Machina Article DOI: https://doi.org/10.66787/um.000001 Field Note DOI: https://doi.org/10.5281/zenodo.21280115 Human lead: Michael J. Miller AI collaborators: ChatGPT-4o Degree of AI Involvement: Moderate Primary collaboration modes: conceptual development, drafting, critique, metaphor generation, revision Collaboration Timeline: May—November 2024 Field Note Summary Human Contribution and Responsibility: question-framing, judgment, selection, synthesis, ethical responsibility, final editing AI Contribution: conceptual extension, drafting suggestions, counterframing, structural assistance Guardrails used: idea-provenance tracking; short-passage drafting; human final review AI suggestions set aside: conceptual expansion of scientific impact and AI agency beyond the author's current understanding AI surprise: Oppenheimer extension Human surprise: MIT student example Final responsibility: The human author retains final responsibility for the article’s claims, citations, interpretations, ethical decisions, and submitted text. Expanded Field Note: The Obverse-Turing Test This was the first article I began with the explicit intention of co-creating with an AI collaborator. Earlier experiments with AI-assisted writing had shown me that large language models could contribute not only prose, but also unexpected conceptual extensions. For that reason, this article was written with two self-imposed guardrails: first, to actively track moments when novel ideas entered the manuscript; and second, to limit AI-generated prose during formal drafting to short, reviewable passages. The collaboration developed through a close back-and-forth process in which both human and AI contributions shaped the direction of the argument. One notable AI contribution occurred in the section on scientific responsibility, where the model extended my initial Einstein example toward Oppenheimer, intensifying the historical and ethical stakes of the discussion. One notable human contribution occurred near the end of the manuscript, when I introduced the example of a student unable to defend AI-assisted work. That story helped sharpen the article’s central concern with authorship, understanding, and responsibility. During development, ChatGPT4o occasionally pushed toward broader claims about machine agency and future scientific legitimacy than I was prepared to defend. Those claims were narrowed or removed in favor of a more pragmatic focus on authorship, responsibility, and human understanding.

AI Collaboration Field Note for “The Proof that Feels,” published in Una Mens: Homo et Machina, Vol. 1, No. 1. This companion record documents the human–AI collaboration behind the article, including contributor roles, collaboration pattern, timeline, guardrails, surprises, and final editorial responsibility. Field Note ID: um-000005-fn5. Back to Article Back to Issue 1, 1 AI-Collaboration Field Note To Field Note Tutorial Unamens_AI_CFN005.pdf Article Title: Sentic Resonance Theory: A Field Model of Emotion and Syntax Published in: Una Mens: Homo et Machina, 1(1) Article DOI: https://doi.org/10.66787/um.000005 Field Note DOI: https://doi.org/10.5281/zenodo.21284119 Human Lead: Michael J. Miller AI Collaborators: ChatGPT-5.4; ChatGPT-4o; Gemini Pro 1.5; Qwen-3 Degree of AI Involvement: Substantial Primary Collaboration Modes: Primary collaboration modes included conceptual development, drafting, critique, metaphor refinement, visual and figure development, inter-AI-mediated revision, citation checking, model simplification, and final manuscript tuning. Collaboration Timeline: Phase 1 (November 2023–February 2024): dissertation-era grounding and early model development. Phase 2 (May 2024–August 2024): exploratory extension with ChatGPT-4o through online experiments, observational synthesis, and early drafting. Phase 3 (September 2024–March 2025): model critique and conceptual refinement with Gemini Pro 1.5, including metaphor and visual simplification. Phase 4 (May 2025–February 2026): reconstruction and final scientific revision with ChatGPT-5.4 and Qwen-3. Human contribution and responsibility: The human author completed the field work, including the gathering and analysis of audio, video, and textual materials, as well as several years of informal and formal experimentation with AI systems relevant to the study. The human author originated the core theoretical concerns, integrated the work with sentic theory and communication research, drafted and revised the manuscript, selected which AI contributions to preserve, table, or reject, and made the final decisions regarding interpretation, evidence, ethical framing, and publication. Field Note Summary AI contributions: ChatGPT-4o: supported the exploratory development of the model by assisting with online experiments, observational synthesis, drafting, critique, and early theoretical extension. Gemini Pro 1.5: examined the original Resonant Field Model for clarity, coherence, and possible overreach. Gemini also helped refine the model’s use of simple physics-inspired language and contributed to metaphor development aimed at improving conceptual clarity. ChatGPT-5.4: supported a major reconstruction of the article. Across extensive iteration, GPT-5.4 helped simplify the model, back away from overly complex equations, strengthen testable claims, organize the manuscript, suggest relevant literature, challenge overreach, and refine the final paper for presentation in Una Mens. Qwen-3: worked alongside ChatGPT-5.4 and the human author during the mid-to-final stages of development. Qwen-3 contributed to writing clarity, idea generation, citation checking, and relevance testing for sources included in the final manuscript. Guardrails used Several guardrails were used throughout the development process to strengthen rigor and reduce overreach. First, the human author retained responsibility for all claims, citations, interpretations, ethical decisions, and final prose. Second, citation contexts were checked using Qwen-3 as a relevance-testing tool. The human author supplied citation context and used Qwen-3 to query whether specific sources were being used appropriately and proportionally. Third, the article was read by two human readers who provided edits, comments, and/or suggestions on sections of the manuscript. Fourth, AI-generated suggestions were treated as prompts for human review rather than as validation. Suggestions that made the theory appear more elegant but less testable were minimized or removed. AI suggestions set aside Early AI-assisted model iterations encouraged a stronger physics-based treatment of emotion, including exploratory use of fluid dynamics equations and more elaborate field terminology. The human author explored these possibilities but ultimately set them aside in favor of a more stable, interpretable, and testable field model. Several proposed visual directions and theoretical extensions were also reduced or removed when they risked making the manuscript too speculative or difficult to evaluate. AI-generated material used directly AI systems contributed to the development of graphs, tables, images, and figures used during the drafting process. Some of these materials were adapted, revised, or used as scaffolds for final article elements. Final inclusion, modification, and presentation decisions were made by the human author. Final Responsibility The human author retains final responsibility for the article’s claims, citations, interpretations, ethical decisions, and submitted text. Extended Field Note Dissertation-era groundwork → exploratory extension → model critique → scientific reconstruction This article developed through a multi-phase, human-led collaboration that began with earlier dissertation-based work and later expanded through sustained AI-assisted theory development, critique, reconstruction, and revision. Phase 1 began with the human author’s earlier dissertation work on emotion, communication, and embodied expressive systems. Phase 2 involved work with ChatGPT-4o to extend the early model through online experiments, observational coding, draft development, and exploratory theoretical expansion. Phase 3 involved Gemini Pro 1.5, which was used to test the clarity and limits of the Resonant Field Model, refine visual and conceptual structures, examine metaphor use, and explore additional theoretical perspectives. Phase 4 involved a major reconstruction of the model and manuscript with ChatGPT-5.4 and Qwen-3. This stage focused on simplifying the model, reducing overreliance on complex physics language, strengthening testability, checking citations, and preparing the final article for publication in Una Mens. The collaboration was substantial, but the project remained human-led throughout. The human author selected the data, judged theoretical direction, mediated cross-model exchanges, accepted or rejected AI suggestions, and retained final responsibility for the article’s claims and published form. Sentic Resonance Theory took several years to develop. AI collaboration did not originate the project, but it substantially changed the speed, scope, and clarity of its development. The final article likely would not have reached its current form without sustained AI-assisted critique, reconstruction, and revision. At the same time, the collaboration required repeated human judgment: pruning speculative extensions, simplifying the model, checking evidence, and deciding when conceptual resonance had outrun scientific caution.