To add more - not reading any text in the paper makes you laser focussed on trying to figure out what the data means without the authors trying to sugar coat anything with their perspective/agenda.
To add more - not reading any text in the paper makes you laser focussed on trying to figure out what the data means without the authors trying to sugar coat anything with their perspective/agenda.
Also, I've noticed that a very large range of papers with faked image data in figures has gone unnoticed by most readers. People look at the figures hoping to see what they want to see and aren't critical enough about the process used to generate them (when I wrote my phd thesis, all figures were programmatically generated by version-controlled code on well-managed data.
The methodology tells you exactly what the people who wrote the paper did. It's where you can see whether they used a sample size of 10 vs 1000, what methods they used to sample their data, the accuracy levels they used in analyzing their data, it's also how a study may be replicated.
Without reading the methods, you can't be sure about any other thing in the paper.
That's the problem i see with a lot of news reporting on science papers. They use the gp's method of reading. Abstract, images and maybe results if they're really trying.
That's how you end up with a lot of sensationalist, contradictory science headlines.
The process was utterly a time waste except for when the paper in question was a survey paper.
now you have sixty+ complex papers, at least a third of which are not actually very important, useful or thorough.. and where are those original, carefully chosen ten you started with ?
side note - the "focus on the figures" reading advice does not scale, since most search is first and foremost with text. Which of the now-eighty papers (and growing, the field is hot) are the ones you cared about and understand.. ? "piled higher and deeper - PhD" indeed!
I've been damned lucky. The worst paper, the one were I had to read most of the citations and quite a few of their citations before I got it was http://conferences.sigcomm.org/sigcomm/1997/papers/p011.ps and yes it took over a month before all the pieces settled into the right corners of my mind. Perhaps more accurately, it took months for my mind to create the right corners for the concepts to settle into.
But that was fine - I knew before I started it was the seminal paper on the subject, and so it would be worth whatever effort it took. The idea of wasting that inordinate amount of time going down that path with one dud after another makes me shudder in horror.
It's a question of who you prioritize while writing a paper. Experts or the interested layman.
If you prioritize people who are already experts in the topic, you write little to no introduction, you get to the results quickly.
If you prioritize the "curious, interested layman" (or university students in their first years), a short introduction to a topic with references will provide enough information to understand the basics and the reader can continue reading the rest of the paper with enough context to understand why the topic is relevant.
Laymen, for example, might need some figures to help understand a topic that experts internalized years ago.
I prefer having occasional well-written review papers, meant for getting non-experts up to speed. Then the domain experts - who are often not experts at writing for laymen - can refer people to those review papers, possibly also with a history delta for what's new since the review.
"For a comprehensive review, see" https://scholar.google.com/scholar?q=%22for+a+comprehensive+...
That's what surveys are for.
My field of cheminformatics, while CS-adjacent, inherits more strongly from the chemistry traditions. The occasional outsider papers from a CS department generally cause my eyes to glaze over if they follow that history-retelling CS tradition.
It's much better than having to look through all the relevant papers in that topic in the last decades in order because they all assume knowledge of the past ones.
Of course, I know there are survey papers, and they can be very useful, but they're typically going to be too general and not specifically oriented to what you need to understand that specific paper you're interested in. Plus, you inherit the biases of whomever compiled the survey, which in my experience are often significant.
For example, in paper I reviewed, the CS authors misread one a paper completely. They wrote something like "method X has been used in cheminformatics before [cite] but the CS literature has improved on that with method Y." But the paper they cited actually used method Y.
Now, the paper itself didn't need that level of detail about the history. That error, and others like it, bugged me because they came across as dilettantes, writing with more assurance than they actually had, and because their history was biased towards the CS methods they knew, which made it feel like they snubbed the cheminformatics methods and treated the previous work in this field as second-class material.
I can't help but think that the process you describe, where someone new to topic must write a history section just to publish, results in a lot of half-baked history, as new people just don't have the experience to really give a good treatment of the history. Instead, they'll see that 15 other papers covered points A-F so they follow the tradition that they need to cover points A-F but with a different slant.
I'm not saying my field is immune to that! There's a well-known observation along the lines that "similar structures tend to have similar properties." Many people will cite a 1990 as the source of that quote. Except that that book doesn't contain that quote. Most people instead know about it second- or third-hand, which has resulted in the common but incorrect practice of making that citation. It's a litmus test I use to tell if the authors really know their history.
Q: If you write multiple papers on a new topic, do you still write histories for each one? Or can you refer to your previous publications for the history?
And the answer is that in general, we do write (short) histories for each paper, unless maybe in short conference papers (limited to 4 pages or so) while it's OK not to write any.
It's not really about history so much as context. Usually, you want to set up a paper with "This is the state of X as it exists right now, but there exists this problem Y. We solve Y by starting from X and making Z advancement."
I think part of the issue is CS is exceptionally young (less than 100 years total, really), and the other part is how rapidly it's advanced and diversified in that time (in terms of individual disciplines even within subfields). Without the context, I can't readily jump to a paper from an adjacent-but-not-directly-relevant subfield without needing to look up a bunch of other stuff. And it's not as straightforward to know where to look to find the relevant information. A paper 10 years old might be state-of-the-art or it might be completely outdated, and it's hard to know which if you're not immersed in that discipline. Having the context in the paper itself is a big help to ameliorating this.
Is it possible they were secretly reporting something else too?