Confidentiality
Confidentiality
What Materials Are Confidential
- Unpublished manuscripts, datasets, supplementary files, and review documents
- Reviewer reports, identities (unless open peer review is adopted), and associated communications
- Internal deliberations, correspondence, decision notes, and appeals
- Personal or institutional information not intended for public disclosure
Our Best Practices for Confidentiality
- Respect for Submissions & Review Materials
All submitted materials, manuscripts, data, reviewer feedback, editorial correspondence, are strictly confidential. They must not be shared, disclosed, or used outside authorized editorial processes without explicit permission.
- Reviewers’ Confidentiality Duties
By accepting a review, reviewers agree to keep the manuscript and related materials private. They must not distribute or upload them to external platforms (including AI systems) unless confidentiality, privacy, and data-use standards are ensured. Reviewers also must never use unpublished data, ideas, or findings from the review for personal research or benefit.
- Editorial Deliberations & Communications
Editors and staff must safeguard decision rationale, reviewer identities, and internal communications. Information gained through editorial work, including methods, ideas, or data, must not be exploited for external use without written permission.
- Post-Review & Rejection Continuity
Confidentiality obligations persist even if a manuscript is rejected, withdrawn, or transferred. Review reports, editorial correspondence, and decision letters may not be disclosed or published without explicit consent.
- Exceptions & Ethical Disclosure
In cases of suspected misconduct (e.g. plagiarism, data fabrication), limited disclosure to institutions, oversight bodies, or funders may occur, but always under controlled conditions, respecting privacy, procedural fairness, and journal policy. If open peer review is used, reviewer identities or reports may be made public, but only under prior policy and consent. Confidentiality does not apply to content already publicly available.
- Technology & Tool Use Safeguards
When using AI tools or external platforms, we require assurances of confidentiality and data protection. Manuscript content, reviewer reports, or internal communication must never be uploaded to systems that do not meet our privacy and security standards.
Duties & Responsibilities
Editors & Staff
- All editorial staff must treat submissions, peer reviews, and related documents as highly confidential.
- Editors must not use manuscript content for personal research or gain.
- Information gained through editorial work, ideas, data, methods, must not be exploited outside the editorial process without explicit, written permission.
- In matters of suspected misconduct, limited disclosures may be made to institutions or oversight bodies under strict confidentiality protocols.
Reviewers
- Upon accepting a review, reviewers commit to treating the manuscript and all supporting materials as confidential and not distributing them to others without permission.
- Reviewers must not use insights, unpublished data, or ideas from the review for personal or external work.
- Reviewers should not upload manuscript content or peer review materials into external or AI systems unless the confidentiality, privacy, and data-use terms of those systems meet our standards.
- In matters of suspected misconduct, limited disclosures may be made to institutions or oversight bodies under strict confidentiality protocols.
Authors
- Authors should regard all reviewer feedback, peer review correspondence, editor comments, and decision letters as confidential, unless the journal explicitly allows disclosure.
- Authors should respect confidentiality limits in any appeals, clarifications, or editorial dialogue.
- When submitting data or sensitive materials (especially involving human subjects), authors must follow robust confidentiality protocols (see the “Data Protection & Confidentiality in Research” section below).
Data Protection & Confidentiality in Research
To further reinforce privacy and trust, authors conducting empirical or human-subject research should observe the following safeguards
Risks, Implications & Vigilance
- Even well-secured systems are vulnerable to data breaches, hacking, or misuse, particularly for human-subject or sensitive data.
- Loss or unauthorized access to private data can harm research participants, breach trust, and damage reputations.
- Every member of a research project shares responsibility for confidentiality, from data collection to archival or disposal.
Exceptions & Ethical Disclosures
We welcome authors, institutions, reviewers, and partners to join us in this green mission
