5 Things: Scientists

Another 5-pointer, inspired by a post I read about the stereotypes of scientists. (I know there are just as many about other professions but scientist is one of my current ones.)

  1. We’re not all “bushy-haired” confused old white dudes.

    It’s amazing that pictures of 19th Century scientists and Einstein have had such an influence on how people portray scientists. This link shows you how academics (researchers in general but a lot of scientists are in here) are shown to children. I wouldn’t have as much of a problem with this if it wasn’t reinforcing a really negative stereotype about the potential uselessness of science (Professors who are not connected to the real world and who do foolish things) and the demography (it’s almost all men and white ones at that) which are more than likely having a significant impact on how kids feel about going into science.

    It’s getting better, as we can see from a Google image search for scientists, which shows a very obvious “odd man out”, but that image search actually throws up our next problem. Can you see what it is?

    Sorry, Albert.

    Sorry, Albert.

  2. We don’t all wear white coats!

    So we may have accepted that there is demographic diversity in science (but it still has to make it through to kid’s books) but that whole white coat thing is reinforced way too frequently. Those white coats are not a uniform, they’re protective clothing. When I was a winemaker, I wore heavy duty dark-coloured cotton clothing for work because I was expecting to get sprayed with wine, cleaning products and water on a regular basis. (Winemaking is like training an alcoholic elephant with a mean sense of humour.) When I was in the lab, if I was handling certain chemicals, I threw on a white coat as part of my protective gear but also to stop it getting on my clothes, because it would permanently stain or bleach them. Now I’m a computer scientist, I’ve hung up my white coat.

    Biological scientists, scientists who work with chemicals or pharmaceuticals – any scientists who work in labs – will wear white coats. Everyone else (and there’s a lot of them) tend not to. Think of it like surgical scrubs – if your GP showed up wearing them in her office then you’d think “what?” and you’d be right.

  3. Science can be a job, a profession, a calling and a hobby – but this varies from person to person.

    There’s the perception of scientist as a job so all-consuming that it robs scientists of the ability to interact with ‘normal’ people, hence stereotypes like the absent-minded Professor or the inhuman, toxic personality of the Cold Scientific Genius. Let’s tear that apart a bit because the vast majority of people in science are just not like that.

    Some jobs can only be done when you are at work. You do the work, in the work environment, then you go home and you do something else. Some jobs can be taken home. The amount of work that you do on your job, outside of your actual required working time – including overtime, is usually an indicator of how much you find it interesting. I didn’t have the facilities to make wine at home but I read a lot about it and tasted a lot of wine as part of my training and my job. (See how much cooler it sounds to say that you are ‘tasting wine’ rather than ‘I drink a lot’?) Some mechanics leave work and relax. Some work on stock cars. It doesn’t have to be any particular kind of job because people all have different interests and different hobbies, which will affect how they separate work and leisure – or blend them.

    Some scientists leave work and don’t do any thinking on things after hours. Some can think on things but not do anything because they don’t have the facilities at home. (The Large Hadron Collider cost close to USD 7 Billion, so no-one has one in their shed.) Some can think and do work at home, including Mathematicians, Computer Scientists, Engineers, Physicists, Chemists (to an extent) and others who will no doubt show up angrily in the comments. Yes, when I’m consumed with a problem, I’m thinking hard and I’m away with the pixies – but that’s because, as a Computer Scientist, I can build an entire universe to work with on my laptop and then test out interesting theories and approaches. But I have many other hobbies and, as anyone who has worked with me on art knows, I can go as deeply down the rabbit hole on selecting typefaces or colours.

    Everyone can appear absent-minded when they’re thinking about something deeply. Scientists are generally employed to think deeply about things but it’s rare that they stay in that state permanently. There are, of course, some exceptions which leads me to…

  4. Not every scientist is some sort of genius.

    Sorry, scientific community, but we all know it’s true. You have to be well-prepared, dedicated and relatively mentally agile to get a PhD but you don’t have to be crazy smart. I raise this because, all too often, I see people backing away from science and scientific books because “they wouldn’t understand it” or “they’re not smart enough for it”. Richard Feynman, an actual genius and great physicist, used to say that if he couldn’t explain it to Freshman at College then the scientific community didn’t understand it well enough. Think about that – he’s basically saying that he expects to be able to explain every well-understood scientific principle to kids fresh out of school.

    The genius stereotype is a not just a problem because it prevents people coming into the field but because it puts so much demand on people already in the field. You could probably name three physicists, at a push, and you’d be talking about some of the ground-shaking members of the field. Involved in work leading up those discoveries, and beyond, are hundreds of thousands of scientists, going about their jobs, doing things that are valuable, interesting and useful, but perhaps not earth-shattering. Do you expect every soldier to be a general? Every bank clerk to become the general manager? Not every scientist will visibly change the world, although many (if not most) will make contributions that build together to change the world.

    Sir Isaac Newton, another famous physicist, referred to the words of Bernard of Chartres when he famously wrote:

    “If I have seen further it is by standing on the sholders [sic] of Giants”

    making the point even more clearly by referring to a previous person’s great statement to then make it himself! But there’s one thing about standing on the shoulders of giants…

  5. There’s often a lot of wrong to get to right.

    Science is evidence-based, which means that it’s what you observe occurring that validates your theories and allows you to develop further ideas about how things work. The problem is that you start from a position of not knowing much, make some suggestions, see if they work, find out where they don’t and then fix up your ideas. This has one difficult side-effect for non-scientists in that scientists can very rarely state certainty (because there may be something that they just haven’t seen yet) and they can’t prove a negative, as you just can’t say something won’t happen because it hasn’t happened yet. (Absence of evidence is not evidence of absence.) This can be perceived as weakness but it’s one of the great strengths of science. We work with evidence that contradicts our theories to develop our theories and extend our understanding. Some things happen rarely and under only very specific circumstances. The Large Hadron Collider was built to find evidence to confirm a theory and, because the correct tool was built, physicists now better understand how our universe works. This is a Good Thing as the last thing we want do is void the warranty through incorrect usage.

    The more complicated the problem, the more likelihood that it will take some time to get it right. We’re very certain about gravity, in most practical senses, and we’re also very confident about evolution. And climate change, for that matter, which will no doubt get me some hate on the comments but the scientific consensus is settled. It’s happening. Can we say absolutely for certain? No, because we’re scientists. Again – strength, not weakness.

    When someone gets it wrong deliberately, and that sadly does happen occasionally, we take it very seriously because that whole “standing on shoulders of giants” is so key to our approach. A disingenuous scientist, like Andrew Wakefield and his shamefully bad and manipulated study on vaccination that has caused so much damage, will take a while to be detected and then we have to deal with the repercussions. The good news is that most of the time we find these people and limit their impact. The bad news is that this can be spun in many ways, especially by compromised scientists, and humans can be swayed by argument rather than fact quite easily.

    The take away from this is that admitting that we need to review a model is something you should regard in the same light as your plane being delayed because of a technical issue. You’d rather we fixed it, immediately and openly, than tried to fly on something we knew might fail.

Let the Denial Begin

It is an awful fact that women are very underrepresented in my discipline, Computer Science, and as an aggregate across my faculty, which includes Engineering and Mathematics (so we’re the Technology, Engineering and Mathematics of STEM). I have heard almost every tired and discredited excuse for why this is the case but what has always angered me is the sheer weight of resistance to any research that (a) clearly demonstrates that bias exists to explain why this occurs, (b) identifies how performance can be manipulated through preconceptions and (c) requires people to consider that we are all more similar than current representation would indicate.

Yes, if I were to look around and say “Women are not going to graduate in large numbers because I see so few of them” then I would be accurate and yet, at the same time, completely missing the point. If I were to turn that around and ask “Why are so few women coming in to my degree?” then I have a useful question and, from various branches of research, the more rocks we turn over, the more we seem to find bias (conscious or otherwise) in both industry and academia that discourages women from participation in STEM.

A paper was recently published in the Proceedings of the National Academy of Sciences of the United States of America (PNAS, to its friends), entitled “Science faculty’s subtle gender biases favor male students”. (PNAS has an open access option but the key graphs and content are also covered in a Scientific American blog article.) The study was simple. Take a job application for  a lab manager position. Assign a name where half of the names are a recognisably male name, the other half are female. (The names John and Jennifer were chosen for this purpose as they had been pre-tested to be equivalent in terms of likability and recogniseability.) Get people to rate the application, including aspects like degree of mentoring offered and salary.

Let me summarise that: the name John or Jennifer is assigned to the same application materials. What we would expect, if there is no bias, is that we would see a similar ranking and equivalent salary offering. (All figures from the original paper, via the SciAm link.)

Oh. It appears that the mere presence of a woman’s name somehow altered reality so that an objective assessment of ability was warped through some sort of … I give up. Humour has escaped me. The name change has resulted in a systematic and significant downgrading of perceived ability. Let me get the next graph out of the way which is the salary offer.

And, equally mysteriously, having the name John is worth over $3,500 more than having the name Jennifer.

I should leap to note that it was both male and female scientists making this classification – which starts to lead us away from outright misogyny and towards ingrained and subtler prejudices. Did people resort to explicitly sexist reasoning to downgrade the candidates? No, they used sound reasoning to argue against the applicant’s competency. Except, of course, we draw back the curtain and suddenly reveal that our sound reasoning works one way when the applicant is a man, another if they are a woman.

Before you think “Oh, they must have targeted a given field, age group or gone after people who do or don’t have tenure”, the field, age and tenure status of the rating professors had no significant effect. This bias is pervasive among faculty, field, age, gender and status. The report also looked at mentoring and, regardless of the rater’s gender, they offered less mentoring to women.

Let’s be blunt. Study after study shows that if there are any gender differences at all, they are so small as to not even vaguely explain what we see in the representation of female students in certain fields and completely fails to explain their reduced progress in later life. However, the bias and stereotypes that people are operating under do not so much predict what will happen as shape what will happen. We are now aware of effects such as Stereotype Threat (Wiki link) that allows us to structure important situations in someone’s life so that the framing of the activity leaves them in a position where they reinforce the negative stereotype because of higher anxiety, relative to a non-stereotyped group. As an example, look at Osborne, Linking Stereotype Threat and Anxiety, where you can actually reduce the performance of girls on a maths test through reminding them that they are girls and that girls tend to do worse on test than boys. Osborne then compared this with a group where the difference was identified but a far more positive statement was made (the participants were told that despite the difference, there were situations where girls performed as well or better). The first scenario (girls do worse) was a high Stereotype Threat scenario (high ST), the second is low ST. Here’s the graph from Wikipedia that is a redrawing of the one in the paper that shows the results.

The effect of Stereotype Threat (ST) on math test scores for girls and boys. Data from Osborne (2007) (via Wikipedia)

That is the impact of an explicit stereotype in action – suddenly, when framed fairly and without an explicit stereotype or implicit bias, we see that people are far more similar than we thought. If anything, we have partially inverted the stereotype.

To return to my first paragraph, I said:

what has always angered me is the sheer weight of resistance to any research that (a) clearly demonstrates that bias exists to explain why this occurs, (b) identifies how performance can be manipulated through preconceptions and (c) requires people to consider that we are all more similar than current representation would indicate.

The PNAS paper, among others, clearly shows that the biasses exist. A simple name change is enough, as long as it’s a woman’s name. The demonstrated existence of stereotype threat shows us how performance can be manipulated through preconception. (And it’s important to note that stereotype threat is as powerful against minorities as woman – anyone who is part of a stereotype can be manipulated through their own increased or reduced anxiety.) So let me finally discuss the consideration of all of this and the title of this post.

I am expecting to get at least one person howling me down. Someone who will tear apart all of this because this cannot, possibly, under any circumstances be true. Someone who will start talking about our “African ancestors” to start arguing the Savanna-distribution of roles, as if our hominid predecessors ever had to apply to be a lab manager anywhere. Most of you, I hope, will read this and know all of this far too well. Some of you will reflect on this and, like me, examine yourself very carefully to find out if you have been using this bias or if you have been framing things, while trying to help, in a way that really didn’t help at all.

Some of you, who are my students, will read this and will see that research that you have done is reflected in these figures. Yes, we treat women differently and we appear, in these circumstances, to treat them less well. This does not, under any circumstances, mean that we have to accept this or, in any way, respect this as an established tradition or a desirable status quo. But the detection of an insidious and pervasive bias, that spans a community, shows us how hard my point (c) actually is.

We must first accept that there is a problem. There is a problem. Denying it will achieve nothing. Arguing minutiae will achieve nothing. We have to change the way that we react and be honest with ourselves that, sometimes, our treasured objectivity is actually nothing of the kind.