It might have been better executed- somehow matching the increased supply of grad students with increase supply of faculty positions, or perhaps just growing it more slowly to let the inequalities equilibrate a bit more. But ultimately, I think it was a good thing, in that it increased the total science being done.
This is contrary to my life experience (even for math or physics PhDs).
What makes a great PhD thesis is to question an insane lot of assumptions of deep results in the field of your PhD thesis, and show that if you base these on a very different foundation, these results generalize to whole different areas; e.g. you found a bridge between seemingly unrelated areas of studies.
On the other hand, managers deeply hate it if you question a lot of assumptions behind the work that you do, whether it is some special case of something deeper, and aren't obedient to the manager's leadership.
In other words: a great PhD program teaches you to think and work all the time on things managers will hate you for.
Here is something on Australia :
https://theconversation.com/australia-has-way-more-phd-gradu...
Key quotes :
The number of PhD completions has been steadily growing over the past two decades, from about 4,000 to about 10,000 per year.
According to our calculations* based on the information available, the cumulative number of people in Australia with a PhD has increased from about 135,000 in 2016 to about 185,000 in 2021.
The incentives are for Universities to get smart young people to do their work cheaply.
What happens to the graduates afterwards ceases to be the University's problem.
The only strange thing about it is that the smart young people are taking so long to figure it out.
I don't think it's so strange. They're smart in their chosen fields, but intelligence is not wisdom or hard experience. Moreover, intelligence breeds confidence, often overconfidence, the idea that you'll be the one to beat the odds. I suppose the same thing happens to young, talented athletes, for example.
Edit: totally unrelated, but PhDs in the West basically seem to be immigration schemes, and universities are happy to play along.
My industry experience taught me the following things:
- In industry, there exist quite some deep, interesting (e.g. math, programming) problems that (unluckily) many people in academia don't have on their radar. These kinds of problems often don't fit into the "boxes" of academic disciplines.
- People in industry are not interested that you attempt to work on a breakthrough on some of these problems (even in your free time) - even if this would give the company millions or even billions of money. They will instead actively be fighting you if you question anything non-shallow.
So to answer your implicitly stated question what you need to succeed in industry: keep your mouth shut, question nothing, and shut off your intelligence. Otherwise you are considered to be a troublemaker.
Can you give some examples?
I have reasons for being a bit cautious on giving details, but some hints on example areas are:
- Understanding the dynamics (mathematics) of some exotic markets that are currently outside of the focus of investors. Very interesting mathematics is involved, but this is too "mathy" for many economists, and (currently, because the rules of the market dynamic still have to be sufficiently understood) too "vague" for many mathematicians who work in academia.
- If you work on data integration problems and/or LoB business applications of some big, conservative companies, you begin to see that many of these problems are instances of deep abstract mathematical structures that are outside of the focus of the academic mathematicians who work in the respective academic area (think for example into the direction of algebraic geometry or algebraic topology): it is too "applied" for them. On the other hand, people in industry have a hate for people seeing these deep abstract patterns that could simplify the applications.
- If you look deeply into some business calculations, you might think that the mathematics that is used there is "easy". But if thus some "theory" describing the business calculations does not need to describe "complicated" things that (academic) mathematicians love to think about - wouldn't this mean that there could exist a great abstraction that simplifies these business calculations a lot in computer programs?
- If stochastics is used in, say, insurance industry in a much more "simple" way than in probability textbooks: couldn't there exist a much "simpler" (but likely very different) theory of stochastics that is sufficient to describe and solve the kind of questions that people in the respective industry care about (though for sure not the kind of questions that academic mathematicians who work in probability theory care about)? Again: people who work in industry hate employees who think about such questions. On the other hand, people in academia are interested in different questions.
- If you look at the source code of some historically grown (but business-critical) business application, you begin to understand that there is no known data structure documented in academic literature that can describe all these things. So you start deriving it on your own. The problem is: business people don't like such deep thinking about the "correct data structure" for their business problems. On the other hand, academic computer scientists are not interested in this question either, because the things that the data structure describes is deeply intrtwined with how the business processes of the respective company work.
Grad students are doing work that would otherwise have to be performed by full professors, who are vastly, vastly more expensive than grad students. So in my opinion, to blame undergraduate tuition on graduate students just seems... bizarre?
https://www.cartoonbrew.com/ideas-commentary/annecy-exposes-...
I’m beginning to think we need to change academia to be driven more by employer demand than by student demand. Stop lying to people, like I was lied to. You probably can’t live your dream. You probably can’t grow up to be what you want. The vast majority of people have to do what society needs.
Let various industries tell the universities what kind of labor force is needed. Then universities should set the numbers of students they accept for those majors accordingly. The time to tell people no is before they spend several years of their lives and a fortune of tuition money in training to do something that will be a dead end.
Children in coal towns don't need to know Shakespeare and shouldn't have aspirations./s
Sounds wasteful.
The children do not need this to serve my coffee or work in the mines.
Business First.
That's a pretty astounding assertion.
Or obvious sarcasm.
It's impossible to know what the future will hold for one's profession. Industry recently demanded "coders" and now a generation of compsci majors are finding the jobs of the present aren't the jobs of the past. A similar thing happened to secretaries and typists. Meanwhile people who were perhaps language or history majors and ended up with stronger communication skills often end up as executives. There are many paths.
Asking career academics to adapt to industrial environments with which many (clinical professors or professors of practice excepted) have limited experience seems like a bridge too far.
What is the source of the assumption that all PhD students want academic posts? The article cited a survey that concluded PhD holders were largely satisfied with their careers, so that population don’t appear to be the squeaky wheels. Other motivations for pursuing a PhD include climbing the mountain because it’s there, moving up the labor schedule, going for a senior position later in one’s career, good relationship with a particular advisor, or delaying entry into the real world.
If it can happen to Einstein, it can happen to the average PhD of today. But, PhDs are over-qualified for the average job (as was Einstein when he worked as a patent clerk), and employers may be reluctant to hire over-qualified folks.
TMWNN•3h ago
gbacon•2h ago
> A 2023 study2 of more than 4,500 PhD graduates in the United Kingdom found that over two-thirds of doctoral graduates were employed outside academia.
If 67-33 isn’t a vast difference, what is?
TMWNN•2h ago
I had to shorten the title to fit HN's character limit.