In STEM fields, there is a great deal of necessary knowledge that simply is not in journals or articles, and is carried forward as institutional knowledge passed around among grad students and professors.
Maybe someday someone clever will figure out how to disseminate that knowledge, but it simply isn’t there yet.
Maybe someday someone clever will figure out how to disseminate that knowledge, but it simply isn’t there yet.
Based on Razib Khan’s blog posts, many cutting edge researchers seem to be pretty active on Twitter where they can talk about their own stuff and keep up on what their colleagues are up to. Grad students on social media will probably respond to someone asking about their subfield if it looks like they know their basics and may be up to something interesting.
The tiny bandwidth is of course a problem. “Professor Z has probably proven math lemma A” fits in a tweet, instruction on lab work rituals not so much.
Clever people who don’t want to pay for plane tickets and tuition might be pretty resourceful though, once they figure out they want to talk with each other to learn what they need to know.
The tiny bandwidth is of course a problem. “Professor Z has probably proven math lemma A” fits in a tweet, instruction on lab work rituals not so much.
That does fit a tweet but knowing that that doesn’t mean that a situation exists where that communication happens.
In many cases you don’t know what you don’t know, so you can’t ask.
For the questions where you can ask StackExchange is great.
Interesting point. Can you give an example of this knowledge?
I’m working on a PhD myself (in engineering), but the main things I feel I get from this are access to top scholars, mentoring, structure, and the chance to talk with others who are interested in learning more and research. One could also have access to difficult to obtain equipment in academia, but a large corporation could also provide such equipment. In principle I don’t think these things are unique to academia.
Sure, not 100% unique to academia, there are also industrial research environments.
My phd was in physics, and there were lots of examples. Weird tricks for aligning optics benches, semi-classical models that gave good order of magnitude estimates despite a lack of rigour, which estimates from the literature were trust worthy (and which estimates were garbage). Biophysics labs and material science lab all sorts of rituals around sample and culture growth and preparation. Many were voodoo, but there were good reasons for a lot of them as well.
Even tricks for using equipment- such and such piece of equipment might need really good impedance matching at one connection, but you could get by being sloppy on other connections because of reasons A, B and C,etc.
A friend of mine in math was stuck trying to prove a lemma for several months when famous professor Y suggested to him that famous professor Z had probably proven it but never bothered to publish.
Jason Mitchell writes in “On the emptiness of failed replications” that there certain knowledge you need to replicate experiments that’s not in the paper:
I have a particular cookbook that I love, and even though I follow the recipes as closely as I can, the food somehow never quite looks as good as it does in the photos. Does this mean that the recipes are deficient, perhaps even that the authors have misrepresented the quality of their food? Or could it be that there is more to great cooking than simply following a recipe? I do wish the authors would specify how many millimeters constitutes a “thinly” sliced onion, or the maximum torque allowed when “fluffing” rice, or even just the acceptable range in degrees Fahrenheit for “medium” heat. They don’t, because they assume that I share tacit knowledge of certain culinary conventions and techniques;
[...]
Likewise, there is more to being a successful experimenter than merely following what’s printed in a method section. Experimenters develop a sense, honed over many years, of how to use a method successfully. Much of this knowledge is implicit. Collecting meaningful neuroimaging data, for example, requires that participants remain near-motionless during scanning, and thus in my lab, we go through great lengths to encourage participants to keep still. We whine about how we will have spent a lot of money for nothing if they move, we plead with them not to sneeze or cough or wiggle their foot while in the scanner, and we deliver frequent pep talks and reminders throughout the session.
How best to give those pep talks would be an example.
Yes I think even in math a lot of what is called “mathematical sophistication” is implicit knowledge that’s hard to communicate without being steeped in the social context in which math is developed and read.
It’s hard to explain, it’s the way you think and talk about math, it’s not about visible signs like notation.
I like the Scott Bakker analogy for magic, there is the visible part of math (formulas, etc.), and the corresponding mental habits. The visible part without the correct way of thinking behind the scenes doesn’t work.
I guess one example is an ontology of “the type of math that’s being done” in one’s head, that lets people quickly figure out what the paper is trying to do after reading relatively little of it.
In STEM fields, there is a great deal of necessary knowledge that simply is not in journals or articles, and is carried forward as institutional knowledge passed around among grad students and professors.
Maybe someday someone clever will figure out how to disseminate that knowledge, but it simply isn’t there yet.
Based on Razib Khan’s blog posts, many cutting edge researchers seem to be pretty active on Twitter where they can talk about their own stuff and keep up on what their colleagues are up to. Grad students on social media will probably respond to someone asking about their subfield if it looks like they know their basics and may be up to something interesting.
The tiny bandwidth is of course a problem. “Professor Z has probably proven math lemma A” fits in a tweet, instruction on lab work rituals not so much.
Clever people who don’t want to pay for plane tickets and tuition might be pretty resourceful though, once they figure out they want to talk with each other to learn what they need to know.
That does fit a tweet but knowing that that doesn’t mean that a situation exists where that communication happens. In many cases you don’t know what you don’t know, so you can’t ask.
For the questions where you can ask StackExchange is great.
Interesting point. Can you give an example of this knowledge?
I’m working on a PhD myself (in engineering), but the main things I feel I get from this are access to top scholars, mentoring, structure, and the chance to talk with others who are interested in learning more and research. One could also have access to difficult to obtain equipment in academia, but a large corporation could also provide such equipment. In principle I don’t think these things are unique to academia.
Sure, not 100% unique to academia, there are also industrial research environments.
My phd was in physics, and there were lots of examples. Weird tricks for aligning optics benches, semi-classical models that gave good order of magnitude estimates despite a lack of rigour, which estimates from the literature were trust worthy (and which estimates were garbage). Biophysics labs and material science lab all sorts of rituals around sample and culture growth and preparation. Many were voodoo, but there were good reasons for a lot of them as well.
Even tricks for using equipment- such and such piece of equipment might need really good impedance matching at one connection, but you could get by being sloppy on other connections because of reasons A, B and C,etc.
A friend of mine in math was stuck trying to prove a lemma for several months when famous professor Y suggested to him that famous professor Z had probably proven it but never bothered to publish.
Jason Mitchell writes in “On the emptiness of failed replications” that there certain knowledge you need to replicate experiments that’s not in the paper:
How best to give those pep talks would be an example.
Yes I think even in math a lot of what is called “mathematical sophistication” is implicit knowledge that’s hard to communicate without being steeped in the social context in which math is developed and read.
As an example, do you mean something like correctly understanding how to “abuse” mathematical notation in a way that remains rigorous?
It’s hard to explain, it’s the way you think and talk about math, it’s not about visible signs like notation.
I like the Scott Bakker analogy for magic, there is the visible part of math (formulas, etc.), and the corresponding mental habits. The visible part without the correct way of thinking behind the scenes doesn’t work.
I guess one example is an ontology of “the type of math that’s being done” in one’s head, that lets people quickly figure out what the paper is trying to do after reading relatively little of it.
The guy is profoundly misguided about the purpose of food X-D
And food photography is a specialized and lucrative field for a reason.