Prompt Science

Call for Contributions – Issue 0

• Author: Submit a human–LLM co-created article.
• Reviewer: Provide peer review for submissions.
• Technical auditor: Help assess reproducibility and model usage.
• Ethics & Governance: Join the working group on LLM use and transparency.
• Staff: Help with editorial, copyediting, or translation tasks.

If you're interested in participating, please fill out the short form below:

Editorial Policies

• Prompt Transparency: All contributions involving LLMs must include a prompt log describing the nature and role of the interactions.
• Human Accountability: Human authors are fully responsible for content validity, originality, and ethical compliance.
• Open Licensing: We support open science. All accepted contributions are published under a CC BY 4.0 license.
• Numerical Results and Verification: Any numerical values, simulations, or quantitative results generated by LLMs must be independently verified through external tools or reproducible methods. Unverified numerical outputs should not be presented as scientific findings unless their verification or analysis is itself part of the study.

Author Guidelines – Prompt Science Journal

Welcome to Prompt Science, a journal dedicated to transparent and reproducible collaboration between humans and large language models (LLMs). Please follow these guidelines carefully when preparing and submitting your manuscript.

Manuscript Types

• Research Articles: Reports on original research involving LLM–human co-creation.
• Case Studies: Descriptions of notable or novel use of LLMs in a scientific workflow.
• Methodological Notes: Innovations in prompt engineering, reproducibility, or collaboration design.
• Reviews and Perspectives: Broader discussions on the implications of LLMs in science.

Article Format

Please use the LaTeX template provided in our Overleaf Project (link to be added). Articles should include:

• Title and short running title.
• Authors and affiliations.
• Abstract (max 250 words).
• Main Text (up to 6000 words): Including Introduction, Methods, Results, Discussion.
• Prompt Log Appendix: Full description or transcript of LLM interactions.
• Disclosure Statement: Describing how LLMs were used and how human oversight was maintained.
• References: Use APA or BibTeX format.

Submission Instructions

1. Prepare your manuscript using our template.
2. Include all required sections and metadata.
3. Submit through our OJSCloud platform at submit.promptscience.org.

Important Notes

• Numerical Outputs: Any numbers generated by LLMs must be explicitly verified unless verification is part of the research question.
• Co-Authorship with LLMs: You may list an LLM as a co-author only if the human author retains full accountability and clearly documents the contribution.
• Reproducibility: We encourage authors to upload code, data, and prompts to an open repository.

For questions, contact us at promptscience@gmail.com

Contact

📬 Email us at: scienceprompt@gmail.com

More contact options will be added soon.

Disclosure of Human–LLM Collaboration

We ask all authors to provide a disclosure explaining how LLMs were used in their work. This includes:

• Which model(s) were used, including provider and version.
• Whether prompts were written by the human author, generated, or adapted from other sources.
• Whether any of the text was generated entirely by the LLM, and if so, which parts.
• If the human author edited, rejected, or rewrote suggestions by the LLM.

Being honest and precise in this disclosure helps build trust and transparency in our new modes of collaboration.

AI Contributor Disclosure Policy

Prompt Science is founded on a core principle: transparency in the use of large language models (LLMs). We recognize that researchers may vary in their familiarity with prompting practices and workflow integration, but all authors are expected to document their LLM usage in a clear, honest, and reproducible manner.

Minimum Disclosure Requirements

• The main prompts used, or a representative example.
• The model name and checkpoint (e.g., GPT-4, Claude Opus, Mistral 7B).
• Key parameters such as temperature, top-p, etc.
• A description of the model’s role in the writing process.

Ethical Expectation

We understand that some authors may be uncertain about how much to disclose or may feel protective of their prompt engineering process. We encourage respectful discretion, but we also strongly emphasize academic integrity. Accurate reporting of AI interaction is not only ethical—it enhances reproducibility, trust, and scholarly dialogue.