Policies
Charges
There are no author processing charges (APCs) or submission charges to publish with JCA. We are proud to participate in the Open Journals Collective.
AI Policy
The Journal expects all authors to maintain full responsibility for the accuracy and integrity of their scholarship. Because generative AI tools cannot be held accountable in this way, we do not allow them to be the attributed author of any work submitted to this journal. With regard to the use of these tools in the research and writing process, we divide their use into two general categories: assistive and generative. Assistive use of AI tools works to transform completed, human-originated and human-produced material (i.e. help me copyedit). Generative use of AI, while often drawing on more-or-less significant amounts of human-produced material, creates a new whole that did not previously exist (i.e. what’s my argument).
Generative uses of AI are not acceptable for any written material submitted to the journal. Any use of assistive AI with data must be disclosed, including models employed, date-ranges of use, and a description of such use. Full chat logs must be made available upon request.
JCA editors consider exceptions for Generative AI in the case of producing code included in a submission. It is important to note, however, that authors are responsible for the accurate functionality and description of any such code, and that this code will be held to JCA’s standards for replicability. Any such use of AI, whether in terms of experimental design or code production, must be disclosed. AI Acknowledgements should be declared in the body of the article or in the first note following the title for editors and reviewers to consider as they prepare review reports and recommendations.
Generative AI cannot be used to produce or alter any data or images that accompany a submission, unless such production/alteration is a clearly discussed element of the project. While authors can use AI to create visualizations based on the author’s data, authors must clearly indicate any such use with models used, date range of use, and purpose of such use.
Peer Review Policy & Ethics
Published articles and datasets in JCA go through peer review. Initial editorial screening is followed by double-blind refereeing by at least two referees. Articles and datasets often go through revisions and resubmission. The target time to decision is between 6-8 weeks. This timeline depends on a volunteer peer-review process by qualified reviewers, and can take, at times, longer if finding volunteers to review a submission is difficult. Simultaneous submission is not allowed under any circumstances. Publication of pre-prints after acceptance is discouraged.
We expect reviews to be written entirely by the human reviewers assigned to them; no prose in the review should be delegated to a human assistant or generative model. Authors who submit manuscripts to JCA expect their submissions will be kept in confidence. Uploading any part of an author’s text to a commercial site breaches that confidence, and we ask reviewers not to do it.
JCA’s default copyright policy allows authors to retain copyright of their material and grants JCA the right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License; use of a commercial site may violate this policy. While local models can ethically be used to explicate a manuscript — as long as the review itself is written by the reviewer — keep in mind that the reviewer’s struggle to understand a text is relevant to the reviewing process. We aim to publish articles that other scholars can understand without automated explication.
Automated translation of a review written by the reviewer constitutes an allowable exception to this policy; in this case, reviewers should include both the text of the original review and the translation along with a note documenting the model used.
Data Ethics Policy
JCA editors recognize that AI technology can enable cultural analytics, including optical character recognition (OCR), image classification, topic modeling, and large-scale data processing among other methods. However, because the ways in which data are initially accessed, handled, reused, combined, published, and deposited for future use also influence scholarship, authors should endeavor to create robust data documentation as part of their research. These ethical data considerations extend to the AI tools and approaches that authors deploy, particularly when applied to digital data that may be scraped from the open web, personally identifiable, or community generated.
Some data is intrinsically identifiable. JCA does not accept research that includes biometric analysis, such as facial recognition, without explicit consent from individuals represented in the dataset. These data ethics guidelines extend to their use of AI. Authors should take care to avoid uploading or processing sensitive data to commercial AI platforms without clarity on ownership and future use.
Data sourcing: Increasingly, JCA receives manuscripts that rely on data scraped from public-facing social platforms, such as Goodreads, Reddit, or LinkedIn. Authors must confirm appropriate research compliance with the platforms’ terms of service, justify the use of any personally identifiable information/pervasive data, and indicate whether their research received ethics approval from their institutional review board (IRB) or equivalent body. We remind authors that most university IRBs encourage researchers to register exempt research projects that use public data. If no review was sought, the rationale should be clearly stated in the manuscript.
Analysis and processing: Tools used in data processing can influence results. AI methods, like all methods, may implicitly rule out certain viable analyses or add extrinsic and possibly biased data. While these effects are an unavoidable consequence of computational tools, they should be made transparent. Authors must provide clear documentation of the AI tools and methods used for handling data. This includes specific details about which AI or machine learning systems are used (including API versions or locally run models); the sources and licensing of data collected or processed; any relevant steps taken to mitigate bias or to protect privacy. Authors should explain why these tools are appropriate for the research design and how they may impact interpreted outputs.
Storage and access: Reproducibility and transparency demands that data be as accessible as possible. Authors should make an effort to secure long-term, publicly accessible storage for data to the extent possible consistent with privacy, intellectual property, and data stewardship. In cases where it is inappropriate to store copies of actual data, authors should document data collection protocols so that similar datasets can be collected and used for replication and reproducibility. For accepted manuscripts, all scripts, data, and supplementary material should be uploaded to JCA’s Dataverse repository before submitting the final version of the article. After we’ve reviewed and published the Dataverse repository, a link to the repository will be included in the article. Do not link to other repositories in the text (e.g. GitHub).
Copyright Policy
Unless otherwise specified, authors retain copyright of material published in the journal and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License (CCBY).
Open Access Policy
JCA is a diamond open access journal which means that all content is freely available without charge to the user and their institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author in accordance with the journal’s Creative Commons licensing policy. This is in accordance with the Budapest Open Access Initiative (BOAI) definition of open access. Authors do not cover processing charges (APCs) or submission processing charges.
Long-term Archiving Policy
All articles are long-term archived by Portico.
Publisher Information
The journal is published by the Center for Digital Humanities, Princeton University. ISSN: 2371-4549
Citations to the journal should take the following format:
Matthew Wilkens, “Genre, Computation, and the Varieties of Twentieth-Century Fiction.” Journal of Cultural Analytics 11 (2016): 1-24. doi: 10.22148/16.009.