It reads like it was written 7 or 8 years ago.

If so, could be great for the field!

Heck for a lot of things you don't even need ML at all. Regular expressions are good enough:

https://www.irit.fr/~Guillaume.Cabanac/problematic-paper-scr...

Inability to replicate is a very general and vague way to phrase the problem, because it implies that non-replicability is a sort of abstract issue that just randomly emerges. But in practice papers that don't replicate, don't for a reason, and often those reasons can be identified in advance given just the paper.

For a trivial example of this see the GRIM and SPRITE programs. If the numbers in a paper aren't even internally consistent, that's a good sign that something has gone wrong and the paper probably won't replicate.

For a less trivial example where you'd benefit from a tool-equipped LLM, consider asking a GPT-4 level AI to cross-check the claims in the abstract, body and conclusion against the data tables. If the claims aren't consistent then you can already assert it won't be possible to replicate because it's not even going to be clear what claim you are attempting to check in the first place.

Of course, the question you get is no longer an actual _headline_, so there's no reason to expect Betteridge's law to apply.

A similar thing happens here: if you take a scientific article, and rearrange its words to change its meaning, what you get is no longer a scientific article, so outside the scope of the classification task "classify scientific articles based on how likely they are to reproduce". It's not even meaningful to ask whether it will reproduce: what you have is no longer a scientific article reporting on research that actually took place and got a certain result, it's merely a bag of words that you generated by permuting the words of an actual scientific article.

Your observations are irrelevant: they cannot be used to tell apart models which accomplish the stated task (correctly classify scientific articles based on how likely they are to reproduce) from those which do not. This doesn't mean the proposed model actually works (I for one think it almost certainly doesn't), but it cannot be strong evidence either way.

It's like somebody proposing a model that can identify an animal based on children's pencil drawings of the animal, then you countering that if you rearrange the photo of a man standing next to a horse into a centaur, the model still returns "horse". Such rearranged, synthetic data is well outside the scope of the inputs the model was meant to handle, so you cannot draw any strong conclusions about the model's ability to perform its actual, advertised task.

[1] https://en.m.wikipedia.org/wiki/Betteridge%27s_law_of_headli...

wouldn't surprise me at all if affect bleeds through word choice and indicates some emotions like uncertainty or overconfidence that are frequently associated with irreplicable results.

but yes, predictive models of any kind should not take the place of more rigorous validation of past results.

Wait a minute. Isn't the abstract the summary? I've read accounts of people digging through a paper and seeing impossible, obviously fabricated numbers. I don't think those types of issues would surface in an abstract.

The amount of slippage in what's being claimed as papers get summarized is far too large in some fields and a major contributor to distrust in science, as it causes authors to try and have cake+eat it. Get challenged on an untrue statement you made to the public? Refer to the body of the paper where the claim isn't made, or is made equivocally, and then blame journalists for "mischaracterizing" your work.

It probably is possible to pick up signs of pseudo-science automatically with ML, but you'd want to use really big GPT-4 style LLMs with large context windows and detailed instructions. We're not quite there yet, but maybe next year.

This reads like a description of phrenology.

It's entirely possible for this to be true and their methodology to still be effective and valid. What that would mean is that the ordering of words adds little predictive power to the classification (whether or not the paper is replicable). I can easily imagine that to be the case. For example, it is a commonly-held belief that it would be a struggle to replicate many papers in the social sciences. If that was the case, this "bag of words" approach would work for those papers, because the mere existence of social science vocabulary would raise the likelihood of failure to replicate.

Secondly this is also the case for

> It also has no understanding of, or the ability to differentiate between, quotation and references within the paper and content written by the authors themselves.

In my example, social science papers would reference social science references. So those references in and of themselves could indicate a higher likelihood of failure to replicate.

One of the most important general themes in NLP is that a lot of the structure that humans find important to understand a document (eg word ordering, parsing structural differences like your text vs reference vs quotation distinction) is sometimes unnecessary for ML to have predictive power.

A common approach is to try something simple like this (bag of words) and then to try something where you add in position vectors (so the model includes word order) or part-of-speech or other structural tagging to see whether that improves classification importance. It's not always required.

What is this is true, it would also mean that the model was of zero or negative value for actual real world use. It is not new insight - least of all to academics - that papers on subjects like cancer cures, consumer prices, or perceptions of well-being are more difficult to replicate than papers on subjects like molecular bonds or the behaviour of steel under torsion.

On the other hand, academics also have an understanding of study power, study construction, how [un]expected the findings are, and the actual significance of the results to the field, all hugely significant information the ML organism does not incorporate. So the model is less useful than the human judgements it purports to replace. Just because a curve fits doesn't mean the model is an improvement on existing understanding

Indeed, as the model described appears to be based on naive keyword associations across a very small number of papers, it's likely less useful even than a Reddit contrarian saying "bah economics" to every economics paper, as at least the Reddit contrarian might occasionally be able to highlight conspicuously suspect-sounding elements of some abstracts, and won't classify a bad economics paper on the steel industry as better than its peers because it contains terms associated with engineering papers and the name of the prestigious institution the author is an undergraduate at

Say you need to classify all of arxiv for some reason. A model like this might give you the classification performance you need while also being easy to build and not too computationally expensive. It might give better out of sample classification performance than a model that attempts to more realistically model the problem domain. Or it might have a distribution of errors that is more tolerable for a particular use case (like say you're less worried about misclassifications of extreme cases but you really really need to be accurate in the middle of the distribution or vice versa for some reason). Or it might be more robust to over fitting or whatever.

Having a wide analytical toolbox is very useful for data science and this type of NLP has its place for some use cases. While it may seem fun to poke holes in the approach taken in this case I don't think the approach is necessarily intrinsically bad in any way. It's obviously (like all tools) bad if misused in the same way that a screwdriver can be used as a hammer, but it will suck.

But if the "map" is actually composed by a ML process parsing texts about London and arranging the place names based on proximity of mentions and cardinal direction references (this might be a fun exercise, might actually result in something that looks surprisingly like a map with the right data sources, and sufficient tuning, and absolutely won't have any real world use), people are going to ask why you trusted it.

To be fair to the authors of the original paper, their actual claims appear to fall some way short of the blog's insinuation that they were proposing that academics should actually use their model decide to spend hundreds of hours of work replicating research papers (which would be the perfect example of misuse of ML). But what they've actually done is a very basic statistical ranking of possibly misclassified subfields without any ability to address systematic error.

When you’re doing prediction for the sake of prediction, the results speak for themselves. There is no inherent necessity for the indicators to be intuitive. There are certainly contexts in which it does matter, though. When accountability matters.

It’s, as you say, “basically just” doing exactly what it’s doing. It works exactly as well as it works. To argue that it’s good or bad based on some implementation detail is … kind of beside the point.

No, it really doesn't work like this