Why get excited about Data Science?

This is an edited version of a talk I gave in Perth for the Innovation Institute, for the opening of Data Science Saturdays, aimed at 12-18 year olds. Huge thanks to the folks at Pawsey Supercomputing Centre, NeSI, and NCI for awesome examples!

Hi there! I’m going to start by recognising that I am coming to you from unceded Wurundjeri land, and pay my respects to the Wurundjeri people and their elders, past, present and emerging. 

My name is Dr Linda McIver, I’m the Founder and Executive Director of The Australian Data Science Education Institute, ADSEI,  a charity dedicated to empowering every student with critical thinking, data literacy, and stem skills in the context of projects that matter. 

I’ve been asked to talk to you today because I get crazy excited about Data Science, and I want you to know why. You’re welcome to mock me for that, but before you do, let me tell you that I wasn’t terribly keen on maths at school. I didn’t understand logarithms, and I found calculus terribly dull. I couldn’t see the point of a lot of the stuff we were learning. I just needed marks on the exam, and that’s not particularly exciting or motivating.

So how did I get from there to someone who’s crazy excited about data science? I founded ADSEI because I found out that Data Science is a superpower. It says so on my shirt, which was a gift from the San Diego Supercomputing Centre. I don’t know how clearly you can see it, but it says “I do Data Science. What’s your superpower?”

Data Science is a superpower because it gives you the power to solve problems. It gives you the power to prove that there are problems – like proving that your classroom is way too hot for compulsory blazers, or showing that the noise level in the gym is actually a health and safety issue (I hated sport at school!) – and it gives you the power to figure out how to fix them, as well as the power to show how well you’ve fixed them. 

So I want to start with some examples of some really amazing data science applications that happen in the real world.

Oddly enough, I’m going to start with my physiotherapist, Joshua Heerey. A lot of physios approach the job somewhat unscientifically. They poke, prod, and wrangle you about, pronounce their diagnosis and then give you some fiendishly painful exercises to do that may or may not solve the problem. When I developed hip problems, I was in a lot of pain. I saw a physio who poked, prodded, and diagnosed me with bursitis. He gave me a few things to do, applied ultrasound and heat, and made no difference at all. He then diagnosed something different, gave me more exercises, and again we achieved nothing. If anything, it was getting worse.

So I went to see Josh. Josh’s approach to physiotherapy is rather different. After listening to the problem and asking questions, Josh measures weakness in different muscle groups using a dynamometer – a force meter.  He uses repeated measurements to ensure accuracy. He finds the weak muscles and records just how weak they are. He also measures the angles each joint can bend to. He assigns exercises (they still hurt, btw) to strengthen the muscles that are weak. Each time I went back he’d measure them again, see which ones were improving, and by how much. In short, he applied data science to physiotherapy, and voila! Together we cured my hip. 

This is a very scientific approach to healthcare. Measure the problem. Work to fix it. Measure it again to see how well the fix has worked. Adjust treatment if necessary. Measure it again. It’s not rocket science, but it absolutely is data science. 

The next story is about a study by Professor Rosalind Picard at MIT that used a wearable device that measured skin conductivity to measure stress (this first study was before wearable devices were common). Your skin conducts more electricity when you sweat, and you sweat when you’re stressed, so in theory higher conductivity means more stress. Of course, there are other reasons why you might be sweating, or why your skin’s conductivity might change, hence the study. They wanted to figure out how good the device was at measuring stress. The device recorded measurements throughout the day, which were then matched against a diary kept by the participant, so that the researchers could track whether people were actually stressed when the data made it look as though they were. 

The researcher loaned the device to a student who wanted to use it to measure his autistic brother’s anxiety levels.  One day this device gave a massive spike in readings. Nothing the researchers could do in the lab could trigger a reading this high. They tried all sorts of stressors and exercise tests, and simply could not get a reading like that. You could show someone a massive tarantula and not get a response like that.

They thought it must be an anomaly. But rather than throw away the data as an outlier, they carefully tracked it back to the matching diary and discovered that the spike in data happened right before an epileptic seizure.

So those researchers could have ignored a value that wasn’t relevant to the study they were doing, or they could have thrown it away as an outlier, but what they did instead was develop this device – the Embrace – a seizure monitoring watch that not only detects epileptic seizures, it can message caregivers to let them know a seizure has occurred, and it also uses accelerometers, or motion sensors, to figure out if the wearer has collapsed. The Embrace has provided epilepsy sufferers with a new level of independence and safety. And it couldn’t have been done without data science.

This next story is about Jennifer Yeung, a Canadian, plane spotter, aerospace engineer, and PhD student. Jennifer’s PhD uses a system called Artemis, which is designed for real time monitoring of neonatal babies, sending data from regional hospitals to specialists elsewhere in the world, so that they can receive the best of healthcare even if their doctors are thousands of kilometers away. In 2019 Jennifer visited Pawsey Supercomputing Centre, and used Artemis with machine learning to track changes in babies’ vital signs BEFORE their health crashed, so that they could receive lifesaving treatment before their condition became critical. Incidentally, Jennifer’s main PhD project is to adapt Artemis to monitor the vital signs of astronauts in real time. How cool is that?! And, again, it’s all data science. 

Now we’re off to New Zealand, where Dr Céline Cattoën-Gilbert  analysed 40 years of climate data on a supercomputer named Maui at New Zealand eResearch Sciences Infrastructure (NeSI) to create high resolution weather and river flow forecasts to predict floods up to 48 hours in advance. This is obviously amazing news for people in the path of those floods, who used to have to wait until the water was lapping at their doorstep to know there was a problem! Now we can use data science to warn people in time to take precautions, or even evacuate if the flood levels are going to be dangerously high.

We tend to think of data as numbers – counting things, measuring things, monitoring things. But data can also be sound and images. For example Dr Giacomo Giorli is an oceanographer at the National Institute of Water and Atmospheric Research (NIWA) in New Zealand. There, his team tracks marine mammal populations around New Zealand through underwater acoustic monitoring, again using NeSI supercomputers. Dr Giorli is particularly interested in whales, and wants to track their movements. But it’s hard to detect and monitor whales 24/7. It’s expensive, often cold and wet, you get seasick, and whales can be just plain hard to find sometimes. If you can place microphones underwater, suddenly you can do 24/7 monitoring from the comfort of your local supercomputer. 

Now off to space! The craters on a planet’s surface tell its history.  Volcanic activity tends to smooth the planet’s surface, by covering it with lava, so the more craters we can see, the older the surface since a volcanic event wiped it ‘clean’. The current database for Mars contains 385,000 identified craters with diameters of 1 km or larger.  But it took at least six years to construct, before it was published in 2012. Planetary scientist Professor Gretchen Benedix at Curtin’s Space Science and Technology Centre used machine learning and the Pawsey Supercomputing Centre’s systems to identify 94 MILLION craters in just 24 hours.  Even cooler, they can now identify craters as small as 5meters across – 200 times more sensitive!

Now let’s get physical. Curtin Graduate student, Jordan Makins, with the help of Pawsey Supercomputing Centre, has developed an open source tool for analysing soccer player performance. Feed the tool data about recent games, and it can tell you how well players are performing, and where their weaknesses are. Data Science is heavily used in sport to try to monitor and improve performance. 

Any trainspotters here? Let’s talk about how Data Science caught Singapore’s rogue train. In 2016 the circle line in Singapore suffered a series of strange disruptions. Trains on the line, apparently at random, lost contact with the control system, which triggered the emergency braking system, leaving the trains dead on the tracks. This is obviously a bit of a problem for a busy train line! The events seemed so random, though, that the train company had no idea what was going on. They called in some data scientists and gave them a dataset containing the date and time of each incident, where it had happened, the ID of the train, and the direction the train was travelling in.

The data scientists tried everything to find a pattern in the data, but it wasn’t always the same train, it didn’t seem to be in the same place, or even the same set of places. It was bizarre.They visualised a whole range of different aspects to the data, using complicated graphs, simple ones, anything they could think of. They crunched all kinds of numbers. Nothing. Eventually they spotted a small pattern in all of the noise: When a train lost signal, another train behind that train but headed in the same direction would often also lose signal directly afterwards. They started to think that perhaps there was a rogue train, causing signal interference with other trains. Complicating their investigation was the fact that the rogue train never interfered with itself, so it did not appear in their data. But that, in itself, was a clue! An extra complication is that a small number of shutdowns are normal, so there was some noise in the data.

Eventually, after a lot of work, they zeroed in on a possible suspect, and checked when that train, Passenger Vehicle 26, was not in service. Lo and behold, very few shutdowns happened during those times! Culprit identified! Passenger Vehicle 26 was repaired to prevent the interference, and the Circle Line went back to normal. Another problem that would have been really hard to solve without data science.

Now let’s talk about something particularly close to my heart, since I’m in Victoria and only just out of lockdown! Professor Linsey Marr is a scientist who proved back in 2011 that the flu was airborne rather than aerosol. Aerosol and airborne might sound the same, but the technical difference is crucially important. Diseases spread by aerosol transmission spread by droplets – particles emitted when you cough or sneeze. Droplets are heavy. They don’t stay in the air, but they CAN land on surfaces and make you sick if you touch those surfaces and then touch your face or your food. They can also land straight in your mouth, nose, and eyes if someone coughs or sneezes nearby. (how gross is that!?) That’s why social distancing is really important with aerosol diseases.

In contrast, diseases that are airborne make you sick if you breathe them in. And, crucially, they stay in the air for much longer. Marr took samples of the air in different rooms, in places like up near ceiling air vents, where droplets simply couldn’t be (because they fall, they don’t fly!), and she found enough flu virus to make people sick. The trick, though, is that she couldn’t get published, because the medical establishment was convinced that the flu was aerosol transmitted.

The reason? In the 1930s a study of tuberculosis found that only particles smaller than 5 microns could infect people with the disease. This somehow got translated into “only particles smaller than 5 microns can be airborne.” 

The thing is that Professor Marr is an expert in airborne pollutants and indoor air systems, and her engineering training told her quite clearly that the physics of this assumption was all wrong. Particles larger than 5 microns hang in the air all the time!

When covid came around, Professor Marr was quite sure it was also airborne, while the WHO and the American CDC among many others were busy saying it was droplet, so social distancing and hand sanitising were promoted as the way to stop the spread, rather than masks and ventilation.

Frustrated, Dr Marr teamed up with a history researcher by the name of ​​Katherine Randall who conducted what was effectively research archaeology – digging down into the history of a topic to figure out where certain ideas come from. Randall discovered that the original tuberculosis study, from the 1930s, did indeed establish that only particles smaller than 5microns can infect a person with tuberculosis, but not because larger particles don’t hang around. Tuberculosis can only make you sick if it gets deep into your lungs, and our lungs very efficiently filter out particles larger than 5 microns well before they get that deep. 

Particles larger than 5 microns DO hang around in the air, and while they can’t give you tuberculosis, they can certainly give you covid19 or the flu, because those can make you sick if they get anywhere in your respiratory system. They don’t need to get anywhere near as deep as tuberculosis does.

Linsey Marr challenged scientific orthodoxy, and she’s one of the heretics I talk about in my book, Raising Heretics, because we need people to challenge orthodoxy, but only on the basis of evidence, data, and rational evaluation. Not on the basis of youtube rabbitholes, reddit, and tiktoks!

We desperately need people who are prepared to be rationally heretical.

Who are prepared to ask “why? “how can we be sure?” “what have we missed?” “how can we do better?” “who are we hurting?” “how can we fix this for everyone?” “how will we know how well it works?”

These questions are often heretical. By asking them, I’ve sometimes made people very unhappy. These questions are uncomfortable. But they are crucial to building an ethical, sustainable, positive future for all of us.

Heresy has been crucial to our scientific development. In the 1840s Ignaz Semmelweis came up with the radical heresy that doctors washing their hands before (and after) surgeries prevented disease. Prior to this doctors went from autopsies to childbirth without washing their hands or changing their clothes. And they wondered why people died. The idea that this could cause disease was considered so ludicrous that it took decades for the idea of washing hands to be accepted. Semmelweis was so ridiculed and pilloried that his colleagues committed him to an asylum where he was beaten and died.

In 1917 Alice C Evans made the laughably heretical suggestion that milk should be heated to a high temperature, or pasteurised, to kill bacteria that could be harmful to humans. She was not taken seriously, being a woman and without a PhD (which, by the way, were not offered to women at the time), and it took over a decade before milk was regularly pasteurized in the US. After her discovery but before its general acceptance, Alice became significantly ill with Undulant fever, a disease caused by one of the bacteria found in raw milk.

In the 1940s and 50s, Barbara McClintock discovered that genes aren’t static sets of instructions passed from generation to generation, but that they can be regulated – turned on and off – by other parts of the genome. She described the reaction to this discovery as “puzzlement, even hostility”, but in the end her research radically changed our understanding of genetics.

In the 1960s, Frances Kelsey of the American Food and Drug Administration refused to approve Thalidomide for use as a morning sickness drug, because she was concerned about the lack of data about whether the drug could cross the placenta, and directly affect babies’ development in the womb. This averted thousands of birth defects in American babies. Sadly, other countries were not so cautious.

More recently, Marshall and Warren’s original paper on ulcers being caused by bacteria rather than stress was rejected and consigned to the bottom 10% of submissions. Barry Marshall eventually drank helicobacter pylorii – the bacteria that causes ulcers – to prove it, thus inducing an ulcer which he then cured with antibiotics.

It might surprise you to know that Florence Nightingale was one of the first data scientists, and her use of statistics actually saved a lot of lives. Nightingale discovered that the way field hospitals were recording deaths was wildly inconsistent, and it made it very difficult to understand why soldiers were dying. By standardising the way they were recorded, she was able to analyse the data and figure out that by far the greatest proportion of soldiers were dying from infections spread in the hospital itself, rather than injuries received in battle. Knowing what the problem actually was meant that they could work to fix it. Once hygiene was improved throughout the hospital, deaths and illnesses dramatically reduced, and many lives were saved.

You can see that there is no practical limit to the ways we can use Data Science to solve problems. To change the world. From sport to disease, from the ocean to space, Data Science is a tool that empowers us to understand the world, and change it for the better. 

We need you to be data scientists. Not necessarily professionally, but to have enough data literacy to ask difficult questions, to challenge the status quo, to be heretics.  And we need you to do it on the basis of evidence and data. 

Fudging the Figures

Much is being made of the fact that Australia has reached 80% first dose vaccinated for covid19. And look, that’s wonderful. But it’s also a bit of a lie. We are not 80% first dose vaccinated. We are 80% of people over 16 first dose vaccinated. As a percentage of the whole population, we are at 65% first dose vaccinated (according to figures I found here, and using the ABS estimate of Australian population of 25,704,340 as at March 2021).

Until recently, vaccination coverage, especially in the context of herd immunity, was reported as a percentage of the total population. That makes sense, because when we’re thinking about herd immunity, we want to know how many people are vaccinated, not how many people over 16. The virus doesn’t stop to check your age before it infects you, or before it gets you to spread it.

Different viruses require different levels of vaccination to achieve what we call herd immunity. For example, according to the WHO herd immunity for the measles is achieved at 95% vaccinated, whereas herd immunity for polio only requires 80% vaccinated. This is probably because different viruses spread in different ways, and have different levels of infectiousness. We’ve seen that with the Delta variant of covid19, which spreads much more easily than the earlier variants of covid.

For our purposes, a useful definition of “herd immunity” is: “enough people are immune to the virus that it no longer causes widespread societal disruption” – such as overloading the health system, and making a lot of people very, very ill. Herd immunity doesn’t eradicate a virus, but it means enough people are immune that the virus is not going to spread unchecked through the population.

The trouble is, we don’t know what level of vaccination we need to achieve herd immunity for covid19, because it’s too new – we simply don’t have the data. But everything we do know about the virus suggests we’re going to need a high level of vaccination, rather than a lower one. And herd immunity is always calculated as a percentage of the total population.

Currently, in Australia, everyone from 12 years and upwards is eligible to be vaccinated. This makes it particularly bizarre to report vaccination percentage relative to 16 years and older. It’s nonsensical. It’s not relevant to herd immunity, not is it relevant to the people currently eligible to be vaccinated. At the very least, we should be reporting as a percentage of the population 12 years and older.

In Australia we currently have 3,825,589 children under 12 – that’s 14.9% of the total population. We know that age makes no difference to ability to catch & spread the virus (despite much shrieking by right wing loons to the contrary), though it does seem to make a difference to average severity – no consolation to those young people who were not average and died, or who suffer long covid still.

Epidemiologists calculating herd immunity use total population – remember that the virus doesn’t check your age before deciding to infect you, or to have you spread it – so, in reality, we need to know the percentage of the total population that’s vaccinated.

The current percentage of the Australian population that’s double vaccinated according to the Sydney Morning Herald is 49.15%. The SMH site is useful because you can toggle between eligible population and total. Because the vaccine is not yet approved for kids under 12, they can’t yet be vaccinated. You could argue, therefore, that it’s only the eligible population we’re interested in.

Sadly, the virus is interested in all of us, regardless of eligibility. That makes the percentage of the total population vaccinated crucially important.

And here’s the thing. The Australian Federal Government, which has pushed all along for living with the virus, for no lockdowns, and for the economy over lives, has decided that 80% of the adult population being vaccinated is quite enough to go back to business as usual. Open borders, international travel, let’s get this rubbish behind us and pretend it never happened. (Actually, further investigation reveals that another document says 80% of the 16+ population. It was released by the Prime Minister’s office on the same day as the one that says 80% of the adult population. It’s actually an attachment at the bottom of that same page. It makes accurate calculations and comparisons rather difficult.)

The total Australian population over 16 is 20,619,959. 80% of that is 16,495,967.2. Note that we’re not even considering the eligible population here (12+). Just the population aged 16 and over. 80% of that population vaccinated amounts to just 64% of the total population. Nowhere near enough for herd immunity.

How about some pie charts? Everybody loves a pie chart, right? Which pie chart makes it look like our vaccination numbers are better? Note these pie charts all show exactly the same vaccination data. Just using different slices of the population to compare against.

Pie chart of vaccination rates reported against total population. Slightly less than half (49.5%) are double vaxxed, a little over a quarter unvaxxed (31.8), and the remaining (18.7%) single vaxxed.
Percentage of total population vaccinated
Pie chart of vaccination rates reported against 12+ population. More than half (58.1%) are double vaxxed, a little under a quarter unvaxxed (19.9%), and the remaining (21.9%) single vaxxed.
Percentage of 12+ population vaccinated
Pie chart of vaccination rates reported against 16+ population. More than half (61.2%) are double vaxxed, a little under a quarter unvaxxed (18.1%), and the remaining (20.7%) single vaxxed.
Percentage of 16+ population vaccinated

The best of the bunch, if you want to make the vaccination rates look better, is the graph for 16+. And our government’s target of 80% double vaxxed doesn’t sound nearly as good when you know it’s only 64% of the total population. Nowhere near any plausible estimate of the numbers we need for herd immunity.

Sadly, the covid19 virus can’t read graphs, and is not interested in massaging the numbers until they make you feel good. It wants to infect as many people as possible. Fooling ourselves – or allowing ourselves to be fooled – only makes that easier.

Is Medicine as Scientific as we think?

Some readers have been surprised by the medical section of #RaisingHeretics, feeling that it must be an exaggeration. So here is another story that might have made it into the book, if all of it had happened in time.

My daughter, Zoe, has had a lot of health issues, including major surgery to re-position her hip sockets. That much was in the book, along with the startling findings that quite a lot of surgeries are not actually evidence based. One thing that didn’t make it into the book is that, when she started having trouble with her hips “popping out” when she walked (the technical term is “subluxing” – they weren’t quite dislocating, but they came pretty close), she got an x-ray and was referred to a sports doctor.

We were lucky that the sports doctor worked closely with a couple of surgeons who specialise in acetabular retroversion, because the radiologist had labelled the resulting scan as “normal”. When Zoe’s surgeon looked at the scan, he measured the angles and showed that Zoe’s hips were about as far from normal as it was possible to be and still be able to walk. Had a doctor looked at the radiologist’s report and taken it seriously, Zoe’s hip problems might not have been diagnosed at all. Once diagnosed, Zoe was able to get specialist physiotherapy plus surgery and can now walk, run, and generally appear as though she has no hip problems at all.

Meanwhile, a GP suggested that Zoe’s collection of symptoms might add up to Ehlers Danlos Syndrome (EDS). She’s been on the waiting list for a Genetics clinic for nearly 3 years, so we have no formal diagnosis as yet. Plus, most doctors know very little about EDS. In fact, you could argue that not much is known about EDS in general, but that’s another set of rants.

Zoe has been managing her symptoms as best she can, needing expert physiotherapy for her shoulders as well as her hips, but we didn’t have a lot of support for a way forward. In lieu of the formal diagnosis, there didn’t seem to be much we could do. In fact, even with a diagnosis it wasn’t clear that there was any significant progress to be made.

In December 2019, though, Zoe started getting increasing headaches and dizziness, frequently needing to sit or lie down quite suddenly after standing. That makes it sound quite controlled, but in fact it’s not unusual to find Zoe sagging against a doorframe or collapsed on the floor without warning. It’s pretty alarming stuff. Fortunately when the EDS diagnosis was first bandied about, I made contact with EDS advocate and all around heroic challenger of injustice and medical orthodoxies, Asher Wolf, who made time for a long phone call that gave me a lot more information about EDS than I had been able to find up til then. When these new symptoms became alarming, I asked them what they thought via twitter direct messages.

Asher, Zoe is getting dizzy spells and headaches. any idea if that can be a symptom of eds? her iron levels are fine.

Asher got back to me immediately.

POTS: postural Orthostatic tachycardia can be an EDS comorbidity. Does she get dizzy when she stands up? Is she low blood pressure? Give her some hydralyte to start with.”

This was the first we had heard of POTS, but the more reading we did, the more Zoe’s symptoms sounded very much like classic POTS, so we got a referral to a paediatric cardiologist (because Zoe was under 18 at the time), with an appointment scheduled for 6 months later.

This is where I have to restrain myself from typing so hard I push the keys right through the laptop and out the underside of the desk. The cardiologist swept into the consulting room, confident of his own magnificence. His assistants did a range of expensive tests before he performed the one test which is diagnostic of POTS. He took Zoe’s pulse while she was sitting, had her stand up, and took it immediately, and then a couple of minutes later.

The diagnostic criteria says POTS is clear when the pulse rate jumps by 30 or more beats per minute on standing, and stays that way for several minutes afterwards. Zoe’s pulse rate did exactly that (though he did not tell us the numbers at the time). At this point, he condescendingly told us that Zoe definitely did not have POTS. He thought Zoe had simple deconditioned during her time in hospital, and though Zoe had made it clear to him that she was very fit and doing a lot of exercise, he thought it was probably the wrong sort of exercise.

And then he charged us a small fortune for the privilege of his condescension.

We seethed all the way home. Given that the human body is not a machine and cannot be relied upon to respond the same way twice, who was to say that Zoe would not meet the diagnostic criteria for POTS if he did the test a second time? (We did not know, at this point, that she HAD met the criteria, and he had either missed it or ignored it.) Why was the diagnostic criteria so narrow anyway? What if it was 29 beats instead of 30? Zoe was clearly still unwell, how could 29 be dismissed as deconditioning while 30 was POTS? My personal theory is that the cardiologist had taken Zoe’s history, decided on his diagnosis, and then done the test, with confirmation bias causing him to ignore or misinterpret the results.

Zoe was, naturally, not keen to find another cardiologist and try again. We were all quite dispirited by the arrogance and condescension, and the implication that we were overreacting to simple “deconditioning” which would right itself given time.

Meanwhile, her symptoms got worse. Eventually, with the support of a new GP, Zoe was referred to an adult cardiologist, whose eyebrows apparently rose dramatically on reading the report. Astonishingly, the original cardiologist’s report noted his findings, which according to his OWN CRITERIA clearly indicated POTS. The new cardiologist did his own tests, and while waiting the requisite few minutes to take Zoe’s pulse again, he commented that even if she did not fit the precise criteria, there was a big gap between “does not have POTS according to strict criteria” and “is perfectly healthy”, and in fact the treatment was the same.

Lo and behold, Zoe does have POTS, and now she also has a treatment plan. What’s more, she has a cardiologist who takes her seriously and does not condescend to her. (I mean, really, if you knew Zoe you would know that condescending to her is dangerous in the extreme. You could lose an arm that way.) This is a low bar for a doctor, I feel, but one that is all too often impossible to clear.

Without Asher Wolf generously sharing their time and expertise, Zoe would likely still be in medical limbo. And that arrogant, ignorant, condescending doctor cost her months of pain and suffering.

What is the moral of this story? First of all, listen to your body, and if what your doctor is telling you doesn’t feel right, find a new one. Secondly, do your own reading. You have to become an expert on your own condition in order to make informed and effective decisions about your care. It has been found over and over again that patients who take an active and informed role in their own care get better outcomes, but too many doctors encourage you to simply do what you’re told, while sneering at Doctor Google.

Certainly, you don’t want to diagnose and treat yourself with only the support of the internet, but you do need to know as much as possible about what’s happening to you in order to even know the right questions to ask your doctor. You are already the expert on your own body. You need to become an expert on any conditions you experience as well. Doctors are all too human and fallible, and medical science is too often more craft than science. The best doctors are the ones who acknowledge that and work with it, using evidence, compassion, and a strong rapport with their patients to achieve the best outcomes.

Why does Education need fixing?

This is an excerpt from Raising Heretics: Teaching Kids to Change the World, available from Booktopia, Amazon, Apple Books, and more.

There are so many signs that our current education system is missing the mark. When my teenager gets frustrated because she doesn’t understand how what she’s learning in maths could ever be useful. When a primary school kid says science is boring. When a high school kid says maths is too hard, or science isn’t for them, or they aren’t smart enough to program a computer. None of these things would happen if education was working. It’s obvious that it’s not.

And that’s unsurprising, since the primary focus of education is a matter of facts, rote learning, and mindless application of procedures. By giving kids “experiments” to do that have known inputs and known results, we teach science as confirmation bias. This trains them that the important thing is to get the right, expected answer (and if you get a different answer, fudge things until it’s right!), rather than exploring the unknown and looking for new things.

Although the importance of STEM is widely acknowledged, it is frequently taught as a matter of tech toys, rather than a crucial tool for solving real problems. This commonly comprises a day of robotics play, or the installation of a maker space where kids can tinker with 3D printers and laser cutters. These toys are frequently error prone and difficult to use, so when kids don’t find them fun, or have trouble using them, they assume that STEM is something they can’t do.

Even when problem solving tools like Design Thinking are introduced in the classroom, they are often only used to solve toy problems that don’t relate to challenges that kids can tackle in real life. Design Thinking plays with trips to Mars, or responding to a famine in Ethiopia, instead of taking one of the many problems in our own schools and communities and empowering kids to solve it. You can’t teach problem solving properly if you skip the really tough part; implementing your solution and then troubleshooting all the ways it doesn’t work the way you thought it would.

By doing this, we tell kids that they can’t make a difference until they are grown up, when we could be giving them the tools to make a positive difference in their world today.

The truth is, with this kind of education we have got really good at turning out obedient kids who follow the rules and do as they are told. And those are not the kind of people we need to overcome the huge crises we’re facing. We need people who are confident, skilled, knowledgeable, and prepared to stand their ground and argue a point. We need people who see things differently, who look for new answers, who understand uncertainty, and who ask hard questions. We need people who are “unbossable”, who don’t do what they’re told without first understanding why it’s the right thing to do. We need people who challenge the status quo. We need people who consider ethics first, rather than as an afterthought or not at all.

Meanwhile, Science has somehow become a partisan political football. Australia’s response to the Covid19 crisis was effective, largely because the Government followed the advice of experts in epidemiology. Unfortunately, we face a larger and more serious existential crisis in the form of climate change, and in this case, the Government is ignoring experts and investing deeply in denialism and cheap grabs for immediate power and profit.

Policy in this country (and most of the world) is largely driven by ideology, powerful lobby groups, and manipulative media organisations, rather than by science and evidence. This kind of destructive behaviour is justified with dodgy data and deeply suspect visualisations, and all too often even the media lack either the scepticism or the skill to call them out.

Inequality is rising under the influence of capitalism-driven globalisation that promises better lives for all via the concept of “trickle down economics”, which the data shows quite clearly does not work. We resist Universal Basic Income on the basis that people would stop working out of laziness, when the data from the trials so far shows not only that people don’t stop working, but also that they become more entrepreneurial. Our governments sell off natural assets, log native forests, privatise essential services like health and education, and give tax cuts to big business despite evidence showing that the best way to stimulate the economy is to give money to poor people. As a population, we swallow the line that it is all for our own benefit, and vote the same people back in.

Social media also drags us by the nose, constructing ever more cunning ways to tie us to their platforms, milk us for data and profit, and manipulate our behaviour, all without our informed consent. Our social and workplace gains are casually undermined by disruptive technologies, while we have no input into, and even less control over, the way they shape our future.

This is why we need a rationally sceptical population. We need to stop being irrationally sceptical of climate science and vaccines and start being rationally sceptical of government policy, business motives, and media beatups.

For more, check out Raising Heretics, available as a paperback or ebook from online bookstores now.

Burning by gaslight

In le Tour de France, most people know that the leader of the race wears the coveted yellow jersey. What is less well known, except to die hard fans of the race, is that the rider coming dead last bears the title of the lanterne rouge – the red lantern. Le Tour is a 22 day race that climbs massive mountains, runs alongside rivers, and through valleys. The cyclists race over three thousand kilometres over that time, with only two rest days. Just crossing the final finish line on the Champs Élysées is an achievement in itself, lanterne rouge or not.

Australia’s efforts to vaccinate the population against Covid19, famously not a race, are now being described by the Prime Fibister as a Gold Medal run. A quick look at the world stats suggests that, in the developed world, Australia is putting in a highly successful bid for the lanterne rouge. The gold medal, or yellow jersey of this run is further out of reach than the peak of l’Alpe d’Huez is for your average weekend lycra fiend. And this is not a situation where just crossing the finish line is a tremendous achievement. Speed matters. Big time. The longer this takes, the more people will die, or become chronically ill.

Our level of vaccination is terribly dangerous, and I don’t intend to go into the reasons for the vaccination stroll out. They have been well chronicled elsewhere. It’s not just the speed (or near terminal lack thereof) that bothers me. It’s that we’re told it’s not slow.

It’s a Gold Medal Run.

We are at the head of the queue.

The Federal Government has done everything right but ATAGI… but the Victorian Government… but Delta… but we’re fine, it’s all fine, we’re going to be fine, because there’s a 3 point… no, a four point (wait, I come in again) plan to exit the pandemic.

And it will be a gas led recovery. Except when it won’t.

Lockdowns are the worst weapon we could use, and totally unnecessary.

But lockdowns are the only weapon we can use, and the only path out.

But also vaccination is our path out, but it’s not a race.

It’s a Gold Medal Run, and I never said it wasn’t a race.

Also this is the strictest lockdown anyone has ever seen, but garden centres are essential retail, and it’s fine to attend your local bowls club.

We couldn’t possibly do any more.

The gaslighting from both state and federal governments has been seriously next level.

But as Melbourne falls into another lockdown and we try to maintain our composure, the thing that is really killing me is not just the gaslighting. It’s that it’s working. It’s that so much of the discourse we’re seeing has been fomented by the murdocracy and their government slaves into Melbourne vs Sydney. It’s that we are directing the rage against each other. We have allowed them to turn this into region against region, us against them, “you don’t understand what we’re going through and you never could“.

Sydney supported Melbourne and now we are being abused. Telling us our lockdowns aren’t strict enough – don’t you see we’re doing everything we can?

What do you mean Sydney supported Melbourne? We are supporting you so hard, when we copped the most horrendous abuse. We were told we had Stockholm Syndrome, that Dan was a Dictator, and that lockdowns were unnecessary.

The thing is, all of those statements are true. Sydney did support Melbourne. And Melbourne is supporting Sydney. There is a flood of love and support in both directions. I received numerous care packages during lockdown last year from friends in Sydney, and constant messages of support.

But there is also a government and murdocracy stoked bonfire of hate, and it’s sucking all of the oxygen out of the discourse. It suits the government, and News Limited, to have us fighting each other, so that we don’t notice what they have done, and are doing to us: merrily shredding our way of life and using it to line their putrid nests.

Sydneysiders, for the most part, are indeed doing everything they can. Just like Melbournians, they are following the rules, staying home, and desperately pining for hugs and freedom. It’s entirely pointless (but oh! so tempting! I’ve done it myself) to tell Sydneysiders that their lockdown isn’t hard enough (which, by the way, we know it’s not, because the numbers are not coming down.). But it’s also cruel. Because Sydneysiders have no control over this, anymore than Melbournians had control over whether we had a curfew or not. It’s the NSW government that is failing to bring the numbers down.

The question of whose lockdown is stricter is entirely meaningless. A lockdown has to be strict enough. And the definition of “strict enough” is very clear – it has to bring the daily number of infections down. NSW’s lockdown is not doing that, so it’s not strict enough. By definition. I don’t care whether Sydney has a curfew or not, or how far they are allowed to go for exercise, or for how long. I don’t care whether we can compare just how tough we each have it. I just want them to be safe. To be free. Just like I want to be safe and free.

Daily Cases: Current Sydney Outbreak vs Melbourne’s second wave. The rolling 14 day average for Sydney is a little lower, but still rising

It has been traumatising to watch the NSW government do all of the things that the Victorian government did that we know don’t work. Locking down individual apartment buildings. Locking down individual suburbs. Keeping kids at school too long. Wanting to send senior kids back to school. It’s terrifying to watch, like a horror film where you know the main character is going to die. All the shouting at the screen, and through the internet, in the world is not going to work. It’s traumatising in part because we fear for our Sydney friends, and in part because we know if it’s loose there, it will get loose here too.

The worst part is that the gaslighting and misinformation are ramping up. There are claims that vaccine hesitancy is the reason we haven’t got good vaccine coverage, so the government has put out ads to encourage young people to get vaccines that are simply not available to them. Meanwhile epidemiologists say that we need to vaccinate 80% of the population to achieve herd immunity, but now the government is saying 80% of the eligible population. This is a very, very different number. 80% of the eligible, ie adult, population is only 62.7% of the entire population. Not, by anyone’s estimate, sufficient to achieve herd immunity.

Now, more than ever, we need to be asking difficult questions of our politicians, and demanding their reasons and evidence for the approaches they take. If they can’t provide evidence, and are unwilling to share their reasoning, then we must hold them to account.

But we must also focus on who has the power. Whose actions have the greatest impact. People attending a party when they should be in lockdown are enraging, sure, and it doesn’t help, but overall it’s not their actions that leave the entire country vulnerable. It’s the ongoing lack of effective quarantine. The lack of vaccine availability. And the failure of governments to make the hard decisions, lest it make them politically vulnerable. Let’s face it, if we had an effective quarantine system that didn’t keep letting the virus out into the community, people going to parties would not be a risk.

Not to mention the whole issue of insecure work and poverty that mean people have to work while displaying symptoms, lest they not earn enough to feed their families (here we are in 2021, and we still have people homeless, hungry, and poor… it’s outrageous, but that’s a whole other blog post).

It’s entirely appropriate to apportion blame right now. But let’s make sure it goes where it’s deserved. To win this race, we have to get everyone over the finish line.