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.
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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.
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If AI involvement varied across phases, report the highest sustained level and let the timeline show the variation.
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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 Systems line.​​​​​​​​​​​​​​​​​​​​​​​
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Una Mens strongly encourages Systems-line attribution for Moderate, Substantial, and Central AI involvement. Authors who have substantive reasons for not naming a specific system should contact the editors to discuss an expanded Field Note alternative.
Light
AI provided limited assistance that does not materially shape the work's intellectual development— 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, in which case Systems credit would be granted.​
​​​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 Systems credit wherever the contribution helped develop, express, test, refine, or organize the human author's core ideas without materially reshaping them.
​​​​​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 Systems credit.
Central
Central: AI involvement was not only substantial but constitutive: the work's final form, argument, or method cannot be accurately described without foregrounding the specific human–AI collaboration that produced it. Removing the AI's contribution would require rebuilding the work from a significantly earlier stage. Central involvement always receives Systems credit. Note: A paper's topic does not determine its involvement level. A paper may study AI collaboration with Light assistance, or study an unrelated topic with Central AI involvement.
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.
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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.
Una Mens AI Collaboration Field Note Checklist​​​
Una Mens AI Collaboration Field Note Tutorial​​​
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Una Mens AI Collaboration Note Template
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Una Mens' AI Collaboration Note Template.
​Most field notes can be completed in 300–800 words, plus a simple timeline.
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​The Field Note complements the article's human author byline and Systems line by preserving a fuller record of how the work developed.
Note: Human authors retain full copyright and legal responsibility for all published content, including any AI-generated material used directly or adapted.
​​Core information
• Article title and publication information
• Human author(s)
• AI System(s), including model/version (use date)
• Systems-line mapping
• Degree of AI involvement
• Collaboration pattern(s)
• System contribution roles(s)
• Human contribution and responsibility
• System contribution description(s)
• AI-generated material used directly, if applicable
• Shared or uncertain provenance, if applicable
• Guardrails used
• Collaboration timeline
• Final responsibility
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Optional but encouraged when relevant
• AI suggestions set aside
• AI surprise
• Human turning point
• Additional notes or 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, provenance, and integrity of the collaborative process.
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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.
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Tip 3 — Contribution Level Should Reflect Consequence, Not Just Amount
The journal’s contribution levels are intended as practical guides, not precise measurements. A contribution is substantial when it materially changes the work’s intellectual core, even if that contribution occurs briefly. A moderate contribution more often helps the human author develop, express, refine, test, or organize ideas without becoming foundational to the work. Authors should describe what the system actually did rather than relying on the level alone.
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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 system contributed, where ideas were shared, uncertain, or difficult to separate, 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.
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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, failed directions, or moments where the collaboration did not improve the work.
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