Seemingly progressive posts such as “Eight women scientists that you need to know about” make my blood boil with anger. True, I had only heard about four of these eight scientists. You should read the article, it’s short, informative, and thought-provoking, and has inspiring historic portraits of the scientists at work – pictures which would have made at least one male relative of theirs mutter “she should be in the kitchen, not in the lab”. Their life histories made for an enjoyable and useful read on a Monday morning (and yes, that counted as research reading, aren’t I lucky). But this article was also a grim reminder of the fact that feminism has frozen in the first mile of a double marathon towards gender equality.
Why do we need to know more about female scientists?
Because of articles like this – how about some more female scientists? Are there really only eight?! And how about trying to read an article about the 800 or more male scientists who have made awesome discoveries that we also need to know about – I’m sure there’s lots that we don’t know about them? But no, that would not make for a nice news item or facebook trending post take because it would take longer than a Monday morning coffee break to read.
Because women who succeed in doing what they like and are good at (scientists or others) are still newsworthy. WTF?! Surely, not the ones who excel in anything related to the home, food or childrearing, that’s not news – women are just naturally good at it, haha.
Because becoming a scientist is a hard thing, and being a woman unfortunately continues to create more invisible barriers to a successful practicing of science than being a man does.
Because, if you take ten minutes to read their bios on Wikipedia, you will notice that most of them were at some point excluded, denied recognition, or discriminated against on the basis of being women. Nothing to do with their research, that was OK – in fact, it was good enough for others to gain credit for it sometimes. WTF?!
Because yesterday, when I heard that some friends have recently had a baby, I instinctively asked “girl or boy?” even though I can’t think of even one reason why the answer to that question should make a difference. But of course it will. (Un)helpful statistics, stereotypes, expectations, images and key words describing the likely life course, appearance, occupation, interests and possible futures open to persons of the male and female genders spring to mind immediately upon determining the sex of a newborn. Will people still care about that when that baby is an adult and wants, for example, to work as an astronomer? I hope not, but I’m afraid that they will.
Oh, just one thing <clambers onto soap box again>. Wikipedia has a special entry on “Female scientists before the 21st Century”. It seems to suggest that it has become easy enough – or at least relatively easier – for women to be scientists in the past couple of decades than it was previously. And it has. But it has not become equally easy to men, and it has not become sufficiently easy.
And then, intersectionality. Combine class, race, wealth, disability, sexual orientation, and a bunch of other things that also mess up with our futures, making sure that the most talented, hard-working or brilliant people don’t have a better chance.
We’re still far from a time when a person’s gender will not be the first thing we notice about them. Yes, there are numerous exceptions – but the point is, we have gone far in promoting exceptions, but we have not yet managed to create a world that supports a regularity, a world in which the particular set of sexual organs, secondary sexual characteristics and learned behaviours have no bearing to how well someone does their job.
Now, off that soapbox and back to my research desk before I evaporate in a puff of angry steam.
P.S. Maria Mitchell – first American professional astronomer who was female. And another reason why Quakers are cool. http://en.wikipedia.org/wiki/Maria_Mitchell
What is this study trying to find out? Perhaps I should spell it out in a separate post. This post basically fits into almost all categories on this blog… but that’s ok, I suppose it is because it’s actually a summary of the blog rationale.
So. (said with a German accent).
What careers and livelihoods do people have in mathematics? How are working lives shaped by working as a mathematician? What would have been different, if they weren’t mathematicians? What does it mean to be a mathematician? What does the lifecourse of a mathematician look like? What key stages, events, breaks, dis/continuities does it have? Why do mathematicians do maths, and what else do they (have to, enjoy to) do? How do others decide that you are ‘fit’ to be a mathematician? How, in practice, does one get to become a mathematician? How does one choose in what bit of mathematics to specialise? How does one learn to speak like a mathematician? I want to know in what terms mathematicians talk about their own lives, how they make sense of their profession. Who is a mathematician?
But I’m not only concerned with individuals. A sociologist cannot understand mathematical lives separated from the institutions that mathematicians ‘inhabit’. How do these institutions function, according to what rules, and who makes up those rules? It is all very good to say that mathematics is the ideal objective science, or at least as close to objective as any field of inquiry can be. But even so, the living and breathing and thinking mathematicians that create and use and explain mathematics also inhabit the same real imperfect world as everyone else; they also negotiate ‘mundane’ things such as finding jobs and writing down the stuff they have thought about; how do they square these two worlds, of ‘mathematics’ and of ‘everyday life’, and how do they translate between them? More importantly, how do they themselves see these two (or maybe just one?) world?
As someone who is not a mathematician, I am an outsider to the world of mathematics. A lot has been written about insider and outsider sociological research, so I won’t expand on this here just yet. I will, later. But I’ll also make sure I discuss this with others, some of whom will be sociologists, and some mathematicians (and maybe some will be neither!).
For now, I define “mathematician” narrowly: as a scientist working professionally in the field of mathematics (regardless of whether it is pure, applied, both, or somewhere in between, or whether the mathematician rejects the pure/applied divide). S/he is usually attached to a university or research institute. S/he may be involved in teaching and/or writing and/or research. At the moment I focus only on mathematicians who do research, although it would be interesting to see alternative pathways, e.g. mathematicians who have wanted to do research, and have ended up branching out in other activities, e.g. specialising in school teaching.
I’m starting this project with two very simple conjectures.
First, working in mathematics is just like any other job. It can be described in terms such as work, employment, career, labour, profession, livelihood, labour market, or vocation. It involves things that one does every day, and things one must (or must not) do, in order to qualify as a mathematician. It combines aspects of intellectual, physical, and emotional labour. It can involve mundane tasks, creativity, management, reading, writing, public speaking, publishing, editing, advising younger people or one’s peers, thinking, discussing, presenting ideas, experiments (physical or imagined), the need to organise people, things and ideas, travel, job-hunting, and much more. Mathematical research and teaching is organised into a sectoral ‘labour market’ with its own sub-labour markets – or at least can be analysed in labour market terms or as a “subsector” of science, as is common nowadays. The mathematical community has its internal formal and informal networks. It has rules and freedoms, and usually-taken-for-granted truths (or conventions, as Boltanski and Thevenot would put it). A sociologist can discover trends and compare mathematical (working) lives to ones in other careers and jobs.
Second, working in mathematics is different from all other jobs, in and out of science. It has specifics which make it special and distinct. Working as a mathematician affects your career, your life course, your options and choices, your difficulties, your social and spatial mobility, potential circles of friends or partners, the meaning you attach to work, the reasons and motivations that urge you to work, and the variety of typical and unique life-paths that you are likely to experience throughout the span of your working life. In other words, being a mathematician shapes your identity and your biography in a unique way, as well as just providing you with something to do and putting bread on the table (how easy it is to earn one’s livelihood as a mathematician, man or woman, is a related question).
Here’s an interesting interview with PhD student in Die Zeit (my translation)
“CAROLA DOERR, 29, gained her PhD from Saarland University and the Max Planck Institute for Informatics.
A wrong decision
Will I wake up some day and just know it for sure? Do I want to work in science or in industry? This decision gave me stomach ache for a long time while I was doing my PhD. I had previously spent two years working for McKinsey who then funded my PhD. My topic was random algorithms. In addition, I held a post at the Max Planck Institute for Informatics [Computer Science] in Saarbrücken and a grant from Google. I never had to worry about money. But I asked myself: Which lifestyle is right for me? Which goes better with family?After submitting my PhD, I first worked part time at McKinsey and the Max Planck Institute, because I still couldn’t make a decision. At some point, however, I realised that it would be difficult to reconcile the lifestyle of a consultant with my desire to have children. At the Max Planck Institute, already two colleagues had children. I asked the director how he assessed my chances for a permanent position in science. He was quite confident . These were three signals for me. Now I live in Paris and work at the Pierre-and-Marie-Curie-University. I have a permanent research job and I can decide freely how much teaching I want to do. My husband is a professor at another university in Paris. Our daughter is seven months old.“
If you read German, here is the full article with five more stories by PhD students in other subjects:
“There is enough stew left in the slow cooker for one of us to have seconds. Or both of us to have one and a halves.”
I spend a lot of time thinking about how it is we learn things. In particular, how people learn mathematics. I spend too much time thinking about it and not enough actually doing it – although last term I changed this by moonlighting as a first year undergraduate maths student. I attended all 1st year, 1st term mathematics lectures, two weekly support classes (in mathematical analysis), plus a more advanced lecture in history of mathematics (for 3rd and 4rd year students) and lots of talks by internal and visiting lecturers. I have not solved the riddle of how we learn, but a friend linked me to a new mathematics blog today and my experience was very similar to this post in which Baez talks about “levels of exellence. I love his idea of playing “teacher/student”. I’ve done that so many times without being able to put an accurate label on it. I would say, however, that there are many more intermediate levels (or “stripes”) in the large ones such as “mastering elementary algebra”. And that you undergo changes during all of those microlevels. Before we know something, it can seem scary and unattainable – but that is because we fail to recognise that with each thing we learn, we change a little and become a little more able to go forward.
One of the central insights in my experiment in learning some mathematics as an adult is the importance of giving up hang ups and learning to get on with stuff without getting stuck on something I don’t understand. It made me realise that people who do science are just like everyone else, apart from the fact that they didn’t stop when they hit their first (or thousandth) wall, and just kept on going. I studied sociology for so many years and I still think I don’t know much. Yet I know much, much more than someone who hasn’t studied it at all. In that sense, sciences are a lot like sport. Learning develops your learning muscles (if you are careful not to get injured). Baez talks about that, too, with the example of swimming.
Suppose that four out of your 402 facebook friends have birthdays today. What’s the
likelihood probability of that happening? It’s not enough to just take (1/365)^3 because that doesn’t take into account how big your sample of facebook friends is. To find out, we need the binomial distribution: http://en.wikipedia.org/wiki/Binomial_distribution (the probability we need is the formula just after the table of contents with n = 402, k = 3, and p = 1/365). Plug the numbers in and you get 402!/6*399! * 48627125 * 0.997^399. How on earth does one calculate this by hand? I got stuck. But then my other half (who is a mathematician and who told I need the binomial distribution in the first place), wrote and emailed me a little Python program to calculate this. A mathematical/programming gift. Better than chocolates! And the likelihood is 0.0739604817154 – or just a bit more than 7%. That’s quite rare indeed, given that with 402 friends we have more people in need of birthdays than there are spare days in the year.
P.S. Actually, this formula is for sampling without replacement – but in this case we sample one friend at a time and then discard the name out of the pile, so there is one name less each time – which means that the draws are not independent. So we actually get a hypergeometric distribution, instead of a binomial one. However, Wikipedia claims that “for N much larger than n, the binomial distribution is a good approximation, and widely used”, so I’m happy with that.
P.P.S. In the first draft I used “likelihood” as a synonym of probability. As you see in this one, that was WRONG. Dammit, I’m such an imprecise social scientist.
(field diary excerpt, 2013)
This morning I went to a Foundations lecture and a Differential equations lecture. Then I did a brief but very important interview and even got hold of some data about the UK academic labour market. Yay! Successful day, and it’s not even lunchtime. But I was hungry, so lunch has already happened and now I’m onto my second coffee of the day. A sociologist is very much a device for making hypotheses about the world out of coffee, just like a mathematician is a device for turning coffee into theorems.
Important and timely interview gone well, new data at hand, and a steaming cup of coffee: maybe that’s the way a mathematician feels when s/he manages to complete a small step of an elephant proof? Are any mathematicians or students reading this? How does completing a step from a proof make you feel?
Now I’m thinking of an invisible elephant standing patiently in the Mathematics common room, waiting to be drawn out of the air. That’s very much how my project looks like in my imagination. Stuff to be found out, it’s already there, and yet I have infinite possibilities for drawing my elephant well or badly.
Oops. I’ve just committed a cardinal sin by mentioning the word “infinite” – and I don’t have an infinity licence yet!
I hear different opinions. Is it merely a matter of preference, and what are the reasons behind the different preferences? It seems that “maths” is preferred by British speakers and “math” by American ones. I don’t know about Canadian and other English speakers. Any ideas?
By the way, the title of this blog is “matters mathematical” (a) to avoid both short versions of the word “mathematics” and (b) because “Coffee and Pi” was already taken. Oh, and it’s a quote from this song:
Peter Higgs, Emeritus Professor at Edinburgh, who gave the name to the Higgs boson, has never sent an email. In a recent interview for the Guardian he called himself “an embarrassment to the department when they did research assessment exercises” and said that it’s “difficult to imagine how I would ever have enough peace and quiet in the present sort of climate to do what I did in 1964.”
I was just watching some lectures by Richard Feynman with my office lunch. I don’t understand most of the physics… yet… but I can now see that he was not only a great writer but also a great lecturer. That made me wonder what would Feynman say about the REF (UK research excellence framework). Actually, having read a couple of his books, I don’t really wonder at all. I think he would have called it “bullshit” and gone on to produce research with far more impact than most of us ever dream of producing, and be very bad at “creating demonstrable impact”. I realise that universities can’t be run by people like Higgs and Feynman. But I do wish that impact assessment wasn’t actually impeding great research and stifling the flight of thought of scientists and researchers.