The researchers in this paper use an astonishingly biased "fake paper detector", requiring only two conditions to be met for any paper to be considered "fake":
1. Use a non-institutional email address, or have a hospital affiliation,
2. Have no international co-authors.
And they acknowledge 86% sensitivity and 44% specificity. It's a coin-toss which biases massively against research from outside the US and Western Europe.
No. They use 400 known fakes and 400 matched (presumed) non-fakes to estimate the sensitivity and specificity of their indicator, then apply that indicator to the full universe, then employ the estimated sensitivity and specificity to the obtained measurement to estimate the approximate actual rate of false papers.
If you know the true prevalence of a disease in a population, and the sensitivity and specificity of your test, you can predict how many positive measurements you obtain. Vice versa, from the (flawed raw) measurement, given sensitivity and specificity, you can estimate the true prevalence.
Furthermore, they’re explicitly saying that “red flagging” by their simple indicator doesn’t mean that the paper is fake, but that it merits higher scrutiny.
ETA: I mean, it could still all be bullshit (by virtue of some bias or so), but you’ll need to argue a bit harder to establish that.
ETA2: Actually, not sure that’s what they’ve done. They might have just reported the raw (very bad) measurement (that they call “potential red flagged fake paper”), without doing the obvious next step outlined above, and without applying any confidence intervals. So, it might actually be a pretty crap paper (though possibly technically correct) coupled with some mediocre reporting layered on top. Isn’t basic statistics taught anymore?
I've worked on research estimating prevalence from imperfect tests, and something that concerns me about this study is that they aren't showing the error bars for their estimates. Typically, you would report a confidence interval for prevalence rather than just a point estimate, and the confidence intervals can often be fairly wide. There's two sources of uncertainty here, the assumed probabilistic nature of the diagnostic test, and uncertainty in our estimates of the sensitivity and specificity.
I think this paper by Peter J Diggle [0], gives a solid methodology. Instead of treating sensitivity and specificity as fixed values using sample estimates, you can model them as each having a beta distribution. In this case these beta distributions can be found using a Bayesian treatment of Bernoulli trials.
Amazing. Reading more carefully, as FabHK pointed out above, they aren't even applying the obvious correction. They're just reporting the positive rate of the imperfect test. I've implemented Diggle's method [0]. When I have time, I'll see if they've provided enough data to do a proper analysis, and maybe write a blog post about it or something.
> Furthermore, they’re explicitly saying that “red flagging” by their simple indicator doesn’t mean that the paper is fake, but that it merits higher scrutiny.
Then they and science should change their sensationalist headline. It's ironic that a paper about fakeness of something uses a borderline misleading title.
So it's ok to lie in a portion of your work? Where do you draw the line? I draw it when someone starts communicating. Being wrong is ok, being deceitful isn't.
Is this headline really deceitful though? Certainly the research is flawed, but the statement "[bad thing] is alarmingly common" is basically just a subjective statement that lets you know what position the author is going to argue.
I will never understand why everyone bends over backwards to justify lazy af journalism. This a magazine which is supposed to do scientific journalism, yet it didn't even mention the points that readers in HN comments were able to figure out on a cursory look. Peer review isn't just the 3 reviewers who accept or reject something in a journal. It's everyone in the scientific community.
You can’t directly calculate both sensitivity and specificity using equal numbers of positives and negatives groups unless the actual population has that ratio.
A completely random test given equal populations results in 50% accuracy and 50% specificity. Things don’t look nearly as good if only 1% of the actual population has the condition.
So, in other words, the signal they get from it is around 70% of the noise, but it's ok because you can indeed do that with good enough statistics?
They better have a flawless methodology, because any tiny problem is enough to ruin their analysis. And well, just flagging almost any paper not from the EU or US as fraud doesn't usually come together with a flawless methodology.
Paper mills are a $3-4 billion dollar industry that is growing rapidly. That money isn't coming from nowhere. There are a lot of fake papers, and the fake paper industry is growing steadily.
So then the question becomes "where are those fake papers being published, and by whom."
You can converge on answers to those questions in a lot of ways. The fake paper detection method is suggested as one tool to aid journals tackle fraud.
If you don't think the conditions are valid, well, ok. But why not? How would you improve on the validation methodology? Obviously having more known fakes would be nice.
Saying the article is "bigoted nonsense" doesn't make a lot of sense without more information (to be fair, I might be lacking crucial context). Are the authors known bigots with history of pushing bigotry? What I read seemed to be a sincere attempt to improve scientific publication practices by identifying the scope and scale of the fraud problem, while also developing means to address it. That doesn't strike me as bigoted nonsense.
That said, the headline of the article is pretty click-baity, and shame on science's editors for that.
> The researchers in this paper use an astonishingly biased "fake paper detector"
I havent looked at the details here, but if you make a prediction model and if that prediction model is robust enough to explain with great accuracy something with 2 or 3 variables, it's not going to be "biased", it's just going to be robust and right more often than not using only these few variables (as long as the training data was broad enough).
1. Use a non-institutional email address, or have a hospital affiliation, 2. Have no international co-authors.
And they acknowledge 86% sensitivity and 44% specificity. It's a coin-toss which biases massively against research from outside the US and Western Europe.
This "paper" is bigoted nonsense.
https://fediscience.org/@ct_bergstrom/110357278154604907