Ten takeaways from ten years at Retraction Watch
retractionwatch.com
retractionwatch.com
I wrote to the conference organizers explaining in very simple terms that it could be easily shown the results were wrong (i.e. "please send this to the original referees" - easy, you would think ...) One of them was sympathetic but said it can't be withdrawn from IEEE, and the other was like "get lost, I don't care". It was quite an eye opener. You really don't know what this is like until you've tried it.
I eventually wrote to the author and his work (Porton Down) saying the same thing and the author reacted quite well. Still, the IEEE site for the papers doesn't say anything. Just my comments on pubpeer. And that doesn't come up in a google search. So there are still people wasting their time reading a result known to be wrong.
https://pubpeer.com/publications/9842D9E91821B0F7CADE333BE9D... https://pubpeer.com/publications/BF67542504DA9118ECBE8869EAF...
The only (obvious) red flag for a potential reader is of course - it was supposedly a very big result, so why was it at an IEEE conference instead of a top combinatorics journal?
Again, not saying that papers can't have problems with methodology, assumptions, or — particularly in the case of theory — soundness, but sometimes, "good" papers can be wrong. These papers sit in the body of literature, and are important context for modern findings. I do wonder if there could be some way to indicate to readers without domain-specific expertise that such a paper has been superseded, so to speak.
[1] This happens in practice in some conferences more than others of course. There are lower-tier conferences where people just dump papers and no kind of actual conversation or intellectual exchange is happening.
"...and the other was like "get lost, I don't care". It was quite an eye opener. You really don't know what this is like until you've tried it."
Mirrors my experience in policy work and legislation. My current 3 takeaways:
Attention economy rules all. I now see everyone, every org as information processors struggling with infoglut. So it's just very hard to even get noticed. Necessary triage makes course corrections very expensive.
Long term, focus on improving feedback loops, reducing transaction costs. Applying those Deming and Drucker style organizational learning ideas to new domains. (I've had very limited success. See point #1.)
Pace yourself, stay focused. Improvement is prohibitively expensive. Mark Twain, Brandolini, and many others, have marveled how silliness defeats truth. Frankly, I burned out. As David I sprinted whereas Goliath was running a marathon. And Goliath has mastered distraction, flooding the zone, provoking a response, patience.
This is not at all surprising to me. Big Pharma has teams of professional statisticians, writers, copy-editors, fact-checkers, etc. and have much more rigorously enforced standards than most academic research institutions. Their bias comes out more in how they frame the study and in the choice of things they study, but I think in general I would trust Big Pharma to be more accurate in reporting their study results than academic labs.
If the incentives are wrong, do your bit to change the incentives!
Retraction Watch is a worthy contribution to the repositories of shared knowledge that are of so much benefit to all of us.
Meaning a retraction that was later found to be in error, resulting in re-publication (or whatever)?
I imagine the bar for retraction is so high it would never happen, but it would be really interesting if it ever did.