As intermittent as this has been, there will now be an even larger mittent. Turning this off for a few months while I transition to new job/city/whatnot. In the mean time, I’ll probably be unable to stay away from others’ comment sections.
1. Lack of consensus is a feature of basic research, not a bug. Plant as many seeds as you can. Do not centralize, do not pick winners in advance.
2. Get the tools we have into as many hands as possible. We are not tool-limited.
3. Fund science well. The best use of our tools and further tool development will occur organically under more secure, risk-friendly conditions.
Another discussion about author rank over at the Drugmonkey Web Log. Commenter GMP raised the point that in collaborations, comp/theory is relegated to the fly-over section of the author list, while the bench scientists are the coastal elites at the beginning and end. Meh. If that is generally true of comp/theory, then it is known in that field and those in the field will judge your work accordingly. That’s why papers and grants and tenure files all involve evaluation by people also in your field.
And this is what is dumb about hand-wringing over this shit and all kinds of altmetric wackaloonery. All disciplines / subdisciplines / subsubdisciplines have their cultural norms, for better or worse. Part of your job as a scientist is to know the norms of your tribe. This desire to standardize, measure, quantify anything and everything across and within disciplines is misbegotten nonsense driven by people who are bean counters at heart, but just want better and better ways of counting, weighing, comparing, and describing beans. If you can’t figure out which beans are which in your discipline just by tasting them, you’re in trouble, and math isn’t going to help. The practice of science is composed of culture – well, many cultures. It’s like trying to come up with statistics to compare the dramatic qualities of various community theater groups. Yeah, you probably could, but who cares? The theater dorks know what’s what.
Obviously, I see the utility of looking at this if you are specifically and professionally interested in publishing practices in science, like you are in library sciences or something. If you are a working scientist, my advice is to ignore it all with extreme prejudice. If you start pay attention to shitty stats, then the stats will become your goal, and you will become an empty shell of a person.
Relatedly: The fallacy of the age of big data is that all data are interesting.
Summary: There are no bad ideas in brain storming! Let’s make grad students pay for their PhDs.
A post by Dr. Zen yesterday morning prompted a Twitter discussion about what’s wrong with the science training system. I’ve been an advocate of limiting intake into PhD programs by banning spending federal research funds on trainee salaries. This would put control over the number of trainees into the hands of funders (either through things like NIH F mechs or by earmarking separate, limited funds for trainees with research grants, e.g. 2 trainees per R01). I would further take the NIH out of the business of choosing WHO gets to do a PhD by allowing them to allocate the funds but have no say in who the PI hires or the program accepts. The main problem with this approach is that it is predicated on the assumption that NIH is competent to do anything differently and gives a fuck about the training pipeline problem, which 1) They barely acknowledge and have shown no willingness to address, 2) They probably don’t see as a problem.
What prompted me to rethink this is the question of whether taxpayer money should be used to fund science PhD training at all. Currently, the number of PhDs who will go on to use their training to conduct publicly-funded research is somewhere under 20%. The largest share of the rest will go to industry, and many more will not become scientists of any kind. I see no compelling reason the taxpayer should foot the bill to train a labor force that will fill the coffers of drug companies by selling the fruits of their labor back to the taxpayer who funded their education.
My proposal is that we make students pay grad student tuition and fees, like other professional higher degrees. If, like medical school or law school or architecture school, doing a science PhD put you in substantial debt, this would solve two problems: 1. It would limit the number of people seeking a PhD by making it a harder choice, rather than a default for people who couldn’t think of anything else to do, thus relieving some of the job market pressures PhDs face. 2. It puts the cost of training PhDs on those who will benefit from their work.
This sounds evil. PhD students already struggle in high CoL areas. But consider this… law students and med students incur large debt because they can expect careers that will allow them to pay it off. One reason medicine and law have such high starting salaries is the recognition that their new employees are normally starting their careers in substantial debt. In both these fields, what this means in economic terms is that hospitals and clinics pay for training their doctors and law firms pay for the training lawyers.
Right now, you’d be crazy to take that risk doing a PhD. But this would change… fast. The number of people doing PhDs would drop quickly to near the number who can find employment as scientists afterward. PIs can spend their grant money on research instead of on salaries. Best case scenario: postdocs become rare to non-existent.
There are a lot of possible objections to this.
1. Everywhere that there are people, we need doctors and lawyers. Less true of scientists, the market for which is more volatile and patchily distributed. Someone from a third-tier law school can still practice some kind of law somewhere. On the other hand, the medical and legal professional societies make some effort to regulate the numbers of trainees in their fields, and scientists could – very theoretically – do the same.
2. Many people with science PhDs do important, “non-traditional” work that might not support a high level of initial debt. Well, this is true of MDs and JDs, too. There could be debt reduction or forgiveness plans for working in the public or non-profit sectors, etc.
3. Who loses? There is a trade off for PIs and departments. Now YOU will have to compete for the best students, who will suddenly be much rarer, rather than them competing for you. You will be judged by career-outcome metrics for your trainees at the lab and department level. Yes, this could greatly exacerbate bullshit like pedigree, glam, and rich-get-richer problems as labor is concentrated in the most “desirable” areas, though I’m not sure these forces could be stronger than the are now. On the other hand, you will get federal student loan money coming in and not waste grant money paying people. But mostly, fuck you because you’ve been benefitting enormously from a shitty system for decades. As I’ve noted before, every attempt at labor reform ever – minimum wage, unions, parental leave, weekends — has been met with the same teeth-gnashing, hair-rending cries that it will reduce competitiveness and efficiency and woe betide us because nothing will ever be the same again with the peasants in the castle. On the other hand, maybe faculty salaries in the sciences would start to look more like those in medicine and law. That shut you up quick, eh?
4. Who else loses? A lot of people who would have done PhDs will not. Maybe we will get the “wrong” people. I don’t know, this doesn’t resonate for me. There is a danger that economically disadvantaged students would be more averse to taking on debt for grad school. However, these issues are addressed in some ways in medicine and law. Another argument is that having well-educated scientists out and about in the world is a good thing. It is, but I’m not sure the numbers were talking about here matter against the general population. If we want a scientifically literate population, the place to start is not having more people get PhDs. That’s like saying if we want incomes to rise we should have more bankers.
Glamdouchery (often conflated with OA, which…let’s just not discuss it) is hot this week, getting some attention over at Drugmonkey and on the Twittings. Yesterday, everyone’s favorite BSD punching bag, @mbeisen, took some jabs from all us chicken-shit pseuds on stuff like glam, pedigree, and their advantages in jobs, grants, and tenure. I’ll preface this with: @mbeisen is awesome. As far as I know he’s a good at some kind of genetics, and without a doubt he’s a great advocate in the community and a founder of PLOS, which just did a great site redesign. Despite his undeniable privilege and loftitude, he is far more vocal and willing to engage on a range of issues than most BSD types, who, if they use it at all, use Twitter for personal press releases and digital self-fluffing. Most importantly, he’s funny, which is all that matters in the end.
But…everyone piled on @mbeisen’s claims that glamour pubs and pedigree become less important over time, e.g. at tenure it’s not a big deal. Especially, pedigree:
This feels wrong. You take the average CV of some BSD or HHMI or young gun assistant prof at an ILAF and check it out. You’ll see something like R1 hot shit lab for PhD and an F31, HHWF postdoc with a Nobeldouche, glam pubs from both. Cranking out PIs 10 years out. Wow! And: acronyms!
But then, consider this: the winner of a 1024 person bracket coin-tossing tournament is someone who just won 10 coin tosses in a row! WHAT ARE THE FUCKING CHANCES?! Wow! In a system designed to pick big winners, we shouldn’t be surprised the big winners are those who got picked.
Then, there’s the self-reportage of folks on the study sections and the search and P&T committees. “We look at the whole candidate. Glam and pedigree, whatever.. .we are serious people and we can see through the fluff and and identify who is a great fit and has lots of potential.” Allow me to suggest that committee members are the last people on earth we should be asking for objective commentary about what “counts” on these committees. We are scientists. How many implicit bias studies do we have to see before we start questioning our own rationalizations for how we make judgments about other people and their work? How did these people end up in your long list or TT in your department in the first place? What helped them land the grant that paid for the work you’re admiring*? So, sure, listen to the oldz tell you what they think mattered to them, then look at the short lists they make.
Fun fact: height is a terrible predictor of who gets into the NBA Hall of Fame. Ergo, height isn’t important in basketball?
My advice to my fellow trainees… if you have a shot at getting a glam publication with the work you’re doing, go for it. Why the fuck not? You’ll get a desk reject from Nature in a shorter amount of time than it takes PLOS One to assign an AE. And if you go for review, your chances actually aren’t that bad compared to wherever else you were going to send it, and it certainly won’t be more scientifically rigorous. And don’t let anyone give you shit or tell you YOU are the one perpetuating glamdouchery. You know that scientifically everything from (e.g.) JNeuro “up” is the same, but the obstacles on the ground ahead of you are not all about science.
Note: I am not saying choose what you do scientifically to try to and produce something you think a glam editor will like or will get you interviewed on Radiolab: 1. You’ll almost certainly fail, 2. You’re a tool.
But: when we’re in a position to judge others, let’s do what we can to dismantle the cult of celebrity and edifice of pedigree glam bullshit this past generation has spent their careers building. Start soon: when you get a BSD paper to review, the instinct is to trust them, to see how their new amazing shit fits into and extends their past amazing shit. They know how to sell their shit. You want to like them and you want the editor to like you and the editor and the BSD have been cozy for years. Maybe their shit inspired you to join the field in the first place! Who knows, but put on your grown up pants and tear them apart like you would your best friend in lab meeting.
*Being a Serious Person, you are of course admiring it completely independently of its publication venue.
Last night we were out for dinner and I had a huge meal with desert and drinks and cheese and everything. I soporifically slumped onto the couch when we got home, and turned on the TV at this exact moment: Keith Moon throwing a drum, just as the good part of “A Quick One…” starts. It’s from The Rolling Stones Rock and Roll Circus, which is overall a dull production that exhibits a lot of the dumbest aesthetics of the 60s. But… this (make sure it starts at 11:02 to recreate my experience):
I never really listen to The Who… but this video is a fantastic reminder of what made the two interesting members of the band (Keith and Pete – see how John and Roger, both dressed head to toe in leather, are looking over to see what the real rock stars are doing) legends, and how if they had quietly retired in 1972 we would be none the poorer.
I get why the Stones and the Who and other aging rockers plodded (and plod) on, lazy and ruined by success. Who could resist? But look at them in 1968! Ignore the jumpy editing and look at Keith’s face while he plays! Pete windmilling for keeps (and still playing an SG)! It’s so easy to forget how phenomenal they were when our memories are crowded with the much, much, much, longer stretch of mediocrity to follow.
I will not make a parable out of this, but just point out that while thinking these thoughts I was reminded of this recent post over at DrugMonkey.