Chinese Academic Institutions Are Polluting the International Academic Community with Low-Quality Peer Reviews (DeepSeek Translated Version)

Using the simplest four-quadrant model, peer review comments can be categorized into four types:

Positive-valuable, Negative-valuable, Positive-worthless, Negative-worthless.

Generally speaking, depending on the quality of the paper and the academic community, the ratio of positive/negative and valuable/worthless may vary slightly. However, one thing remains constant: the vast majority of negative and worthless review comments are written in pure Chinglish.

These types of reviews convey nothing but the following messages:

I didn’t read your paper carefully—I just skimmed it last-minute before the review deadline.

I didn’t really understand what you were saying, but your Table 1 results seem unimpressive.

I’m not great at English, nor am I proficient with a keyboard, so three sentences should suffice.

I don’t recognize any names as potential friends who might have submitted this, so let’s just reject it. Heh.

In the past, the volume of such reviews was still manageable. A sane and responsible Area Chair (AC) could discern the paper’s actual quality and make an objective judgment. But now, as Chinese academic institutions mass-produce assembly-line PhDs and low-quality papers that flood the academic world, the number of such reviews has skyrocketed. More than 50% of the feedback now falls into the “Not even not valuable” category. Even worse, the number of rational and responsible ACs is dwindling, while those spouting nonsense in Chinglish is rapidly increasing.

A few years ago, ICLR boldly adopted open peer review to improve the double-blind review experience. However, the practice of making reviews open—but not revealing reviewers—has only emboldened the Chinglish-speaking reviewers. If they don’t care even when their identities could be exposed, then anonymous conference management tools (CMT) are nothing more than a cesspool to them.

Different environments breed different people. This particular environment is simply incapable of fostering GPT-level innovation. Enough said.