Published in the Republica on March 23, 2021.
Plagiarism is a manifestation of a deeper problem in academia: Of publishing for the sake of publishing, and of rewarding it regardless.
“Do I need to cite a source if a plagiarism detection tool doesn’t show that I’ve borrowed an author’s words?” asked a participant at a research workshop recently. “I will have to rewrite much of my article if that’s the case.”
I was not surprised. Instead, I started wondering where the question was coming from. In op-eds and other discussions, I’ve seen plagiarism treated as a problem of stealing words (rather than ideas). For instance, in a recent, highly nuanced, proposal for apology as a mode of redemption for those who have plagiarized in the past, the author casually claimed that there are now technological tools for “easily” identifying and preventing cases. Academic leaders and institutional policies alike, I remembered, exude the same incredible hope.
What’s even worse, issues about quality and integrity of research, not to mention its social value and responsibility, are overlooked in discussions of its originality. Across South Asia and the rest of the global south, there is an increasingly misguided focus on the product of publication—rather than on the ends to which it is a means—reflecting what current policies demand and reward. Even when “impact” is talked about, it simply refers to proxy measures of quality of the product, such as the number of citations (which may be mere name-dropping, including one’s own). Indeed, that is what “journal impact factor” means. When “quality” is used explicitly, that too simply means that the venue is “international” (or not locally located) or that the product is in English (instead of a local) language. If these critiques sound radical, it’s because the status quo is absurd. It is because it rewards publications that may have no significant value.
It is not just that someone can reap rewards by simply paraphrasing or summarizing others’ ideas. They can also make progress by fabricating or manipulating data. Either way, the magic of technology fails whenever scholars fail to ask what specific tasks specific technologies can do and how, where they can be bypassed, what to learn from using them.