1. Suggesting Reviewers. We do not want you to suggest reviewers. We will ignore all suggestions, positive or negative.

2. Self-Reference. Several common practices of eliminating self-references create more difficulty for authors than they solve. What is most important is that reviewers can accurately assess your manuscript.

1. If important details about your dataset have already been published elsewhere and you want to refer to them, either give the full citation, as anyone else would, or replicate these details in an appendix for reviewers.
2. If important details about your dataset are in an unpublished paper, or a dissertation, and you want to refer to them, make an appendix for reviewers.
3. If you want to refer to past published work in which you have expressed similar ideas or have laid the tracks for the current work, then do so if someone else who had written this paper would reasonably cite these pieces. If one would not expect anyone else who had written the current paper to cite your earlier work, it is probably a misuse of the power of reference to include these self-citations.

In general, either your reviewers know you and your work, in which case eliminating a self-citation actually is identifying, not de-identifying, or they don’t, and they’re not going to care who you are. If your argument relies on support from work elsewhere, and yet you only refer to this in a way that cannot be checked by reviewers, they—and we—are likely to be skeptical. Papers that tell readers to see supporting information that they cannot see are likely to be rejected.

3. Originality. If your paper seems to be similar to papers/books that you have already published, or currently have under review, or are readying for publication, please prepare a brief originality statement indicating what is present only in the current submission. This will speed up the processing of your manuscript. If you do not include such a statement, we will assume that there is little to no overlap with other work, and we reserve the right to reverse a positive decision if we later learn otherwise. This includes books as well as articles; the days when it was considered acceptable to publish nearly the name material simultaneously as chapter and article are long gone.

4. Open Science I: Code. It is not now a requirement that articles that do formal (statistical) data analysis share their code and data. However, we now consider it expected that code be archived in the Dataverse account associated with the AJS—even if the data allowing reproduction is not. We do understand that not all researchers, especially graduate students embarking on their first major project, proceed in a sufficiently mechanical fashion to make this always appropriate, but we think that it is in almost all cases consonant with current best practices that researchers preserve a log that allows them to share the code. You do not need to comment it or make it useful to others (especially if you cannot share your data); simply allowing others to confirm that there is no disagreement between what you say you did and what you did will be a big step forward. It will be expected, but not required, that such code be given upon submission of the final draft as a condition of acceptance. If you do not wish to share your code, you should say upon first submission that you will not share your code and explain why. A great paper that has a good reason for not sharing code can certainly be published. But, all other things being equal, we will tend to prefer submissions that share the code. You will retain copyright over the code.

 5. Open Science II: Data. There are four possibilities for the type of data used in a standard quantitative paper: (1) public data to which all have, with minimal effort, access (e.g., the GSS, ANES, and so on); (2) shared data with some restriction (e.g., more sensitive information from GSS or Add-Health); (3) proprietary data collected by individuals or small groups who have agreed to share it with you (as the author); (4) data you have collected yourself. In possibility (1), you need not share your data, and in possibility (2), you cannot share it. For (3), you are of course beholden to those who generated the data, but we strongly ask you to try to get permission from them to make the data, even if in simplified form, public. We are always concerned at the use of data that neither you, nor your reviewers, knows inside and out. For (4), it is not necessary that you share the data you have gathered—and this definitely does not apply to ethnographic data or in-depth interview data, in which sharing the data may identify respondents and is not in keeping with standard practices.[1] However, we will tend to prefer, and trust, papers based on data that you are willing to share. Further, you might consider that the AJS is the appropriate paper for the capstone piece of your project, something that would be published just around the time when you are ready to share the data with others.

If you are indeed willing to share the data, you should say so upon first submission, as the reviewers and/or editorial board may take this into account. “Sharing the data” does not mean that you will have a footnote saying that readers can contact you for the data—it means placing it in the Dataverse archives that are associated with the AJS, and including a link/reference in your article.

In some cases, it is impractical or unethical for you to share the original data. But there may be stages in between sharing and not sharing. You may share reduced forms of the data: perhaps aggregated at some level, perhaps in the form of correlation matrices, perhaps split into separate data files to combat unicity, perhaps by using a model to simulate data that is close to yours in many ways.

6. Open Science III: Pre-registration. In no way is pre-registration required or even in all cases favored. However, particularly in experimental work where effect heterogeneity is being explored, pre-registration helps reviewers tell the difference between a very well fleshed out theoretical understanding (on the one hand) and post-hoc explanations of the failure of the effect to hold more generally (on the other). If you are going to assert preregistration, you must either include an appendix containing the preregistration documents, or supply a link to an anonymized version. You should explain any deviations from the scheme, or any omitted results, either in the main text or in a special appendix for reviewers.  Post-hoc explanation is not necessarily a bad explanation, but statistics from multiple tests are different from statistics for single tests, and reviewers and readers need to know which they have before them. And pre-registration is not about guessing the right lotto numbers: If you pre-register a hypothesis and then find the opposite, this may turn out to be a very successful project and produce an interesting paper.

7. Publication Rights. The AJS does not publish papers that have already been published elsewhere, and that includes self-publishing—which is what you do if you give your paper to the public (that’s why we call it publication), or otherwise apply a license to substantively similar versions. This is different from pre-posting:  it is common for authors to post preprint versions of their papers in progress. So long as the paper isn’t reviewed, given a “doi” as part of your posting, or assigned a number as part of a series, this poses no problem for later review and publication.  However, to publish in the AJS, you must be able sign the AJS’s publication agreement upon acceptance. If, prior to submission to the AJS, you have assigned copyright to another entity or have applied a Creative Commons (or other) type of license that prevents copyright transfer or prevents an eventual copyright holder from exercising necessary licensing rights, you may not submit or publish that paper in the AJS. To read more about what does and does not constitute prior publication, please see the University of Chicago Press’s author rights page.

While some journals have APC-funded options for “Gold” open access (allowing the final “Version of Record” to be freely available to others, generally requiring that the author pay a fee to publish an article under a CC BY type of license), the AJS does not currently offer this option. Instead, the AJS supports “Green” open access policies. The University of Chicago Press’s support of Green OA options enables authors to meet funder requirements. You can read more about our Green OA policies here: https://www.journals.uchicago.edu/journals/ajs/open.

8. For more information about the AJS’s and University of Chicago Press’s author rights policies, including definitions of key concepts, terms, manuscript versions, and license types, please visit: https://www.journals.uchicago.edu/cont/jrnl_rights.

[1] However, even here there are options—Michele Lamont shared coded data from her interviews, and these have been made use of by others.