What drug am I on this morning? Let me tell you, it's the anti-tech tech drug.
As you may have guessed, many new technologies are prone to abuse e.g. using generative AI to produce deep fakes, or what I learned earlier this week, QR code scams (link). The tech consensus is that we don't see any problems because one can use technology to combat such risks. I call this the anti-tech tech drug.
In the academic community, the digitization of knowledge has had profound impacts. One such impact is the creation of "metrics" - a whole field called bibliometrics - that purportedly measure the influence of specific researchers and research papers. What did I say about metrics? See this post from long time ago called "The inevitable perversion of metrics", which later got expanded into Chapters 1-2 of Numbersense (link).
Someone decided it would be a good idea to measure research contribution just like we measure the value of online advertising. Both count things like citations, clicks, downloads, views, etc. Citations are like hyperlinks. Google, whose initial algorithm was entirely based on counting hyperlinks, quickly discovered that hyperlinking is easily gamed. From very early on, humans invented multiple schemes to defeat it as a measure of importance, e.g. link exchanges, barters, buying links, selling links.
Not surprisingly, smart researchers have used myriad schemes to game bibliometrics. According to this recent article in Significance magazine, some less-than-honest researchers are flooding the zone with "fake" papers, written by bots. Just imagine having generative AI generate large numbers of "new" papers by feeding it already published articles.
Some anti-tech tech companies say don't worry, we have technology to combat such abuse. What is this tech? It's a plagiarism detection tool that can find copied contents.
What happens? The tricksters adapted.
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Plagiarism detectors look for segments of text that have been seen before. What kind of text will evade detection? Unusual phrases, what we might call statistically unlikely phrases. Well, someone already wrote software that injects unusual phrases to replace common ones. The tricksters use such software to continue gaming the metrics.
Thus, these "rephrasing tools" have invented "P esteem" instead of "p value", "bosom peril" in lieu of "breast cancer", "flag to clamor" which means "signal to noise".
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Even so, the authors of the Significance article were too polite to say out loud what's really broken about the peer review process, which has failed to reject many such papers. They reported that more than a thousand papers retrieved from PubMed contain the words "statically significant" instead of "statistically significant" in its title or abstract.
The authors respond to this enormous red flag by musing that "somehow, these goobledegook sentences also pass through peer review."
I can think of two likely scenarios:
- some peer reviewers did not even open the papers they were supposed to opine upon
- some editors colluded with authors in a publishing scam
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