Self-plagiarism occurs when an author reuses portions of their previous writings in subsequent research papers. Occasionally, the derived paper is simply a re-titled and reformatted version of the original one, but more frequently it is assembled from bits and pieces of previous work.
It is our belief that self-plagiarism is detrimental to scientific progress and bad for our academic community. Flooding conferences and journals with near-identical papers makes searching for information relevant to a particular topic harder than it has to be. It also rewards those authors who are able to break down their results into overlapping least-publishable-units over those who publish each result only once. Finally, whenever a self-plagiarized paper is allowed to be published, another, more deserving paper, is not.
Splat also refers to
textual self-plagiarism by cryptomnesia (reusing ones own previously published text while unaware of its existence)
(I know all about this) Green takes a more nuanced view and has some interesting discussion.
I’m surprised by the fact that self-plagiarism hasn’t been addressed before. I’ve seen quite a few cases where the same author has two papers that differ by one global Find and Replace, plus a corresponding adjustment in the notation.
At the same time, I don’t think this issue can be understood simply in terms of matching blocks of text. If, for example, Professor X writes ten papers on Problem Y, the summary of the literature and the description of the problem are going to be pretty much the same each time, even if there’s a substantial new contribution in each paper. Insisting that these pieces of necessary boilerplate be rewritten for each new paper seems rather pointless, and the alternative of citing or quoting the first paper for such material is silly.
In any case, there are worse sins along these lines than (partial) self-repetition. The biggest problem is the analog of “PhD variation”, papers which derive the consequences of marginal changes in a model the author has already analysed to the point where it can deliver no new insights.
The other problem with the Splat analysis is that it’s very much in the old world where everything that matters is in journal articles. Increasingly, though, important ideas are going to be aired first in newer media like blogs, before being refined into journal articles.