Yeah...I saw this about an hour ago, and bit my tongue. But it is dismaying that apparently the reverence for science is so high around here that it extends to downvoting criticisms of science as a process and sociological phenomenon to oblivion.
Maybe it will help these poor fellows if I say look, I have a PhD, I work in research, and I agree with everything krick says. Yes, papers are unnecessarily jargonistic and borderline illegible. Yes, there are massive problems with the incentive structure in science. We openly say science is "publish or perish"; how can we not expect that to incentivize lower paper quality, irreproducibility, and status signalling in the form of unnecessary jargon? Even if we assume the noblest of intentions for every single scientist, which is...idealistic.
spaceseaman is equating (IMO unfairly) criticism of the process of science in its current US manifestation with some kind of disrespect towards its obvious beneficial outcomes and motives. The whole thing IS full of inefficiency, and that's not solely because science is hard. Taxpayers have a right to demand that we don't waste their money and perhaps even to present our findings in a way they can understand with a reasonable application of effort (ideally not paywalled as well).
Ultimately the issue is that we somehow got fixated on the p-value, which (roughly and imprecisely speaking) quantifies the probability that there is any effect, even a small one, rather than using effect size estimates which estimate both the magnitude of the observed effect and the uncertainty in that estimate.
Using p-values as our primary metric means an overemphasis on finding small effects (which are usually not clinically relevant anyway) and unduly low focus on things with big effects.
If an effect is real, but very small, that too may well cause replicability problems because it suggests the effect may not be very robust to small changes in experimental conditions, whereas a big effect would be more likely to be robust.
If you think about the really important scientific findings -- the ones that made a big impact and are indisputably true -- statistics usually aren't necessary to prove them, because the effect size is so large it is simply obvious. I'm not against using statistics anyway, of course, but the point is that we should be looking mainly for effects with big effect sizes if we are after important findings, IMO. It is only a major bonus that big effect sizes are most likely to be replicable.
Because a very common type of hypothesis is along the lines of "gene X is important in disease Y" or "priming people with words will affect psychological outcome Z".
The opposite hypothesis is the null hypothesis which is "gene X is NOT important in disease Y" or "priming DOESN'T affect outcome Z".
Since we assume that most interventions will not affect most outcomes, these are much less surprising and interesting results. They are seen as "water is wet" type of findings and are thus hard to publish because no one is interested.
Now if your hypothesis is something like "X will cause Y to go up" and you actually find it causes Y to go down, that IS publishable. It is only when X has no effect on Y that you will have problems.
Yes, every time I hear people talking about how evil or difficult threads are, I just think back to my frequent use of OpenMP. It is really quite easy to take a single-threaded program and make it multithreaded with OMP -- so long as the jobs only read, and do not write shared state.
Usually I will write a program that does the following:
1. Initialize global read-only data structures
2. Parallelize jobs across STDIN lines (or whatever)
3. Output results within a #pragma omp critical block
It works wonders, and is literally 5 additional lines of code to make a single-threaded program multithreaded with OMP. But only for some types of program. The concept is very similar to what GNU parallel does.
In short, threads are not the problem per se, they are just the wrong tool for the job if you have a multiple-readers multiple-writers situation. Message passing, databases, or something else are more appropriate in those cases. But you will pry my read only shared memory space out of my cold, dead hands.
I have to thank you for your "Ph.D. Grind" memoir. There were many lessons in it, but the lesson I took from it, as I recall from reading it several years ago, was basically that you have to discard perfectionism to get anything done.
I'm not sure if that was even an intended message, but that's what I took from it, and I thought back to it during the dark hours of dissertation writing when I was repeatedly thinking "this isn't good enough". I also remembered the lesson that one should be willing to be flexible in the sense that the dissertation doesn't have to be exactly (or at all) what you originally intended it to be.
Thanks for your interest. I've actually met him once -- a friend of his I was talking to over beers introduced us -- but at the time, he was seemingly more interested in his pending date with the blonde he had just picked up than talking with a lowly graduate student. Can't say I blame him :) His papers are excellent, though. A more philosophical and broad approach is needed in aging, I think. He has mellowed a lot from the exaggerated claims he was making in the early 2000s. Maybe he saw my poster which covered an early version of this work, but we didn't talk about that.
The best aging researcher alive right now IMO though is Jim Kirkland. I've had the good fortune to work a little with him and the man is a living encyclopedia. His brilliance is obvious even in a conference full of PhDs.
Well the project arose like this. I got interested in aging halfway through the PhD. I started collaborating heavily with aging people at my institution. But there are so many papers and so much data to get informed about the area.
So I wondered "is there some semi-empirical way to find out what is 'most important' in aging so I can focus my future efforts on that?"
The solution I hit on was to take all the available gene expression data and to build a system to ask "what genes/pathways/systems change most strongly and consistently with age across species, experimental conditions, and tissues"? This would be a "core aging signature", if it exists. Obviously this is only one of many ways to answer my question and neglects epigenetics, proteomics, etc, although we're currently extending the system to DNA methylation. There is not enough high-throughput proteomics data to make it possible to do this with protein yet. We do not use sequencing for now because it is much more of a processing burden and human RNA is behind dbGaP embargo. And at the time I started this, there really wasn't that much of it compared to GEO.
My boss's interests are much more general than aging, so he encouraged me to develop the system to be more generic while still answering my question, which I did. It became a general meta-analysis system for asking "what genes change expression with <arbitrary condition> across the available experiments in GEO?" We found other things we could do with such a huge amount of expression data, and some of them are in Chapter 5.
I would say the system itself is 80-90% done. But sadly I did not get to a really detailed analysis of aging yet, although my findings so far on that are in Chapter 4.
Oh, God, I'm not sure anyone is totally happy with their dissertation, but I guess I should get over it. I got about 2/3 of what I wanted to done. I'm continuing with the project, though.
Not really. My salary was funded by my PI and he was happy to keep someone at my skill level as long as I wanted (and at a bargain salary to boot).
From my department and committee, I started to feel rumblings around mid-year-6 of the "you should get on with it" lines. But they weren't trying to force me out or anything, it was more like concern.
I was spending (and still do) a much bigger percentage of my time on collaborative work than my peers, and they were concerned I wasn't adequately focused on my own career. But that wasn't the case at all -- I was doing what I thought was in my best interests, especially considering my field (bioinformatics) is inherently highly collaborative compared to the wet-lab stuff going on around me.
However, there were some institution-level reasons I cut it off at 7. After that, the institution's policy is that you have to start re-taking some classes you already took (and passed). It was irritating to always have to go to an irrelevant journal club every week (I was in a department that really had nothing to do with my research). Also, I had some appealing opportunities available if I finished when I did. But in no way was I "forced out".
Wow. I just defended a few weeks ago, at 7 years. And honestly, until about year 5, I felt no rush whatsoever to get out. Not that I was derping around -- I finished with 20+ papers -- but I was enjoying it, and I realized that once I was out, two undesirable things would happen:
- I would have to start running the grant treadmill (and indeed, I'm just in the middle of finalizing a training grant that has as one of its requirements...that I must write another grant)
- In the US NIH, there is a 10 year "New Investigator" status that lasts 10 years from the PhD, where you are preferred for certain types of grant awards. So I reasoned it is much better to graduate late and start this clock from a strong position than to rush up the hierarchy and find yourself victim of the Peter Principle.
So for me, delay was a conscious strategy. Only downside was I had to live on a modest stipend. It was worth it for me, though. Another point that I'm making is that "productivity" is not equivalent to "graduating/getting promoted as fast as possible".
As for TFA, I would say that amazing quantities of work can get done by pushing yourself, deadlines, etc, but the creative work that lays groundwork for future growth only occurs during low-stress periods. I try, therefore, to set up alternating periods of both types.
I get why game theory is an attractive model to explain human behavior. But I always wonder why they invent the game, and try to explain behavior in terms of a particular model, rather than using real behavior to fit model parameters, so you could get (pseudo-) empirical numbers for "payoffs" etc.
Or maybe another way of putting it is that clearly rational and human behavior varies with the payoff structure, so it would make sense to include that as another variable.
> I think it is fair to label people inclined to sympathy with Nazi ideas as Nazi sympathizers.
OK, fair enough, but that's not what you originally said, nor is it what people are doing when they throw that term at me. They're (sometimes intentionally) conflating advocacy of free speech for repugnant ideas with advocacy of those ideas themselves. But I appreciate your openness to try to see my position.
Seems like a distinction without a difference. Even if you run a website on your own server, you are still using the ISP's hardware, so they could make the exact same argument. And hosting services are so popular exactly because ISPs have discouraged self-hosting.
Right for service totally without restrictions, no, but "viewpoint-based discrimination" or other types of discrimination are a separate legal category for a reason. "Shirts and shoes required" is different from "whites only" (or, for that matter, "liberals only") on a restaurant door. We allow the former but not the latter, and I think that's reasonable.
I wrote a much longer comment, but long story short:
I'm in medical research. Do I think antivaxxers should be able to publish in medical journals? No (unless they performed an experiment that passes peer review, etc).
Do I think they should be able to run their own websites, have nonviolent meetings or rallies without losing their jobs? Yes, I do. Even though antivaxxers are doing far more harm than neo-Nazis are.
Also, "scientific/academic consensus" and "rationality" are not synonyms.
I would sympathize with them on this issue. This is actually the second time this week I have been called a Nazi sympathizer essentially for believing everyone, including Nazis, should be able to air their views in such a way that interested people can hear them. (i.e., they don't have the right to spam everyone, but they shouldn't be blacklisted from businesses otherwise open to everyone just because of the content of their speech).
Being unironically called a "Nazi sympathizer" is by far the most chilling thing to me that has happened of all the political events that have occurred in the last few years. It is also sad that I have to add a disclaimer that I have voted Democrat in the last decade of elections, am not a bigot, etc, etc, (not that someone using this sort of language would believe me).
Do people who say these sorts of things not realize the massive irony of accusing people who defend basic rights of a generally detested group of being "Nazi sympathizers"?
Hmm, perhaps instead of "rationale", I should have said "philosophy behind" the 1st Amendment. Obviously the 1st Amendment is not directly intended to cover entities other than the government. But almost everything in the Bill of Rights is essentially intended to protect the rights of minorities against the tyranny of the majority, whose representative is the government. It is not necessary to protect popular views, from either the government or from others.
This philosophy is as old as the university and modern science. It is certainly no younger than the "free speech, but not consequences" argument, which I read as a thinly-veiled way to threaten unpopular views with unspecified "consequences".
> "private businesses can't have moderation"
I wasn't arguing that they can't. Only that it can be unwise and a net negative for our society for them to choose to do so. I would draw a distinction between trolling/spam and someone honestly espousing an unpopular view.
It is true, some kind of filtering is needed, because there's too much information for any individual to absorb. The question is, who (if not me myself) is doing the filtering and what are their incentives? dang's incentives are aligned with mine: good-quality technical discussion, and people aren't blacklisted purely for espousing unpopular views.
The incentives of corporations are not. Google, Facebook, and in this instance, Namecheap, are presumably taking these actions to safeguard their image and maximize profit, not to actually make their users maximally informed. There is a conflict of interest here.
Finally, I think all this becomes much more important when it comes to political speech, because people have a marked tendency to want to shout down, ignore, misrepresent, or otherwise not engage with political views that make them uncomfortable. Look how the media misrepresented the Damore memo. I take no position on the memo itself, but do assert that people are better off knowing actual arguments from all sides, rather than side A blacklisting side B and then giving strawman representations of what B thinks.
This viewpoint confuses the legal meaning of the First Amendment, which indeed only applies to the government, with its rationale.
The rationale is the theory that having a diversity of viewpoints, including some that are dangerous or repugnant, is the best approach towards a robust civil society and intellectual growth. If you disagree with something you hear or read, you can ignore it, or you can criticize it, but banning speech causes two undesirable outcomes:
1) The speech will continue anyway, but not in the public eye, where it can be criticized
2) The popularity of an idea is different from the correctness of an idea. It has happened many times that an unpopular or even heretical idea has turned out to be right.
Almost certainly these people are no Galileo. But this approach to free speech is intended to protect the Galileos of the world. It should be kept in mind that talk of revolution in the U.S. during the late 1700s would have been considered extremely dangerous and repugnant by many as well.
Oh, yeah. Health issues. For me, it got to the point where every morning I would wake up and vomit. It got to be part of my morning routine to just get it over with before heading straight to work. Otherwise it might happen in the car.
I think the apathy that accompanies depression and burnout is your body's way of telling you: "your current set of goals and priorities is really, REALLY not working for me, and you need to change something". What leads to burnout is ignoring these increasingly desperate signals from your body and brain.
I just defended on Wednesday, and had a similar experience, but not quite as extreme. For me, the pressure was internal rather than external -- my committee was (in retrospect) OK with my progress and ready to let me go, but I wasn't. I felt my dissertation wasn't "ready", I didn't want to have my name on something that wasn't "good enough".
But even though the circumstances were different, it led me to the same place eventually. After months of the same kind of lifestyle, I decided I just didn't care anymore, was ready to go be a farmer or something if they failed me, and would just submit what I had. Obviously it worked out fine (so far). But it will be some time before I recover from that level of stress. I developed fairly severe alcoholism that I am just now starting to step down from. As happens with burnout, my efficiency was terrible during this period, which made everything worse.
I hear this kind of experience is pretty common for a dissertation -- everyone goes through some twisted version of the stages of grief before submission.
Maybe it will help these poor fellows if I say look, I have a PhD, I work in research, and I agree with everything krick says. Yes, papers are unnecessarily jargonistic and borderline illegible. Yes, there are massive problems with the incentive structure in science. We openly say science is "publish or perish"; how can we not expect that to incentivize lower paper quality, irreproducibility, and status signalling in the form of unnecessary jargon? Even if we assume the noblest of intentions for every single scientist, which is...idealistic.
spaceseaman is equating (IMO unfairly) criticism of the process of science in its current US manifestation with some kind of disrespect towards its obvious beneficial outcomes and motives. The whole thing IS full of inefficiency, and that's not solely because science is hard. Taxpayers have a right to demand that we don't waste their money and perhaps even to present our findings in a way they can understand with a reasonable application of effort (ideally not paywalled as well).