The various formats of peer review

Different peer review formats may be preferred depending on which of these expectations take precedence, as not all of these expectations can be easily combined. While being relatively homogeneous at the outset and during its first centuries of existence, peer review has currently grown into a vast number of different formats, diversifying the practice of quality control in science. By now, so many formats and forms of the system exist that some have even claimed that peer review has become too diverse to speak of it as one single system (Biagioli, 2002; Pontille & Torny, 2015; Rennie, 2003).  

In some research disciplines, most notably the natural and biomedical sciences, the prevailing review format is that of single-blind (reviewers know the identities of the authors, but not vice versa), pre-publication (review takes place before the article is published online) review. However, other review systems such as double-blind (author and reviewer identities are mutually blinded to each other) or post-publication review (when articles are first published and only later reviewed by a potentially large community) have currently gained support.

The distinguishing elements between various review formats may be classified along four dimensions: (i) The relative timing of the review within the publication process (e.g. pre- or post-publication review, or even pre-submission review), (ii) the extent to which review is open or closed (e.g. are reviewers anonymous? Are review reports published or not?), (iii) the level of specialisation in the review process (e.g. are specialist statistics reviewers involved?), and (iv) the extent to which technological advancements have been introduced to the review process (e.g. are plagiarism detection, image manipulation, or statistics checkers used?).

Some formats are specifically designed to foster research integrity. For example, some journals use review formats that only judge on methodological quality, rather than significance or relevance of the results. This aims to take away incentives for researchers to cook their data in order to find spectacular results. Moreover, some institutes specifically focus on integrity during their review process, adopting various digital tools to specifically filter fraudulent from non-fraudulent research.