So, I’m on sabbatical this year.
This does not mean, as some erroneously assume, that I just get to not work for awhile. I have a lab; I kind of have to be there…
What this does mean, is that I have the flexibility to set my own schedule, and can blatantly ignore meetings and other college service.
I had thought about going to Six Flags for awhile this summer. I enjoy amusement parks, and I just hadn’t been to one in a few years. I entertained the thought of bringing my summer students, but wasn’t sure it would actually be fun for all of them. (And there is nothing worse than being in a “fun place” and not enjoying it.) So instead, I decided to play hooky from the college convocation, the day after Labor Day. Instead of sitting and sweating in academic regalia for a few hours, I would be sweating in a t-shirt and sports bra. (I promise this is relevant.) A friend of mine, an engineering professor at a nearby school, joined me, as their classes started a couple days later than ours.
I also though I might make this an academic exercise, and log accelerometer data during the rides. We had both ordered the PocketLab Voyager, but alas neither mine nor hers had arrived yet in the mail.
The good news is I had recently discovered the phyphox app on for the iPhone. It can record the sensor data from a whole bunch of the iPhone sensors. For acceleration the data is logged at a rate of 100 Hz, in all three axes.
The park was really empty, which was awesome. We never had to wait in line more than a few minutes. We got a LOT of rides in. This relative emptiness made it a little less awkward for my sensor mounting protocol. The phone display has to be unlocked for phyphox to record data. So I used the sports bra method, which (TMI) I also use for running.
Though this day, I had to be a bit more careful, as I tried to place the phone on cardinal axes. Phone display faced out from the chest (+z). The y-axis was up/down, the x-axis was side-to-side.
Start data collection, put phone in bra, get buckled/locked into the ride, ride goes. So I often have several minutes of data logged corresponding to just sitting still, waiting for the ride to begin.
I’ll go through the data I got for three categories of rides: Kiddie rides, roller coasters, and swing rides.
“The sloping, curving figure-8 track looks just like a whip in the air. But you’ll dart around the track as light and agile as a cat, with long, cruising twists that’ll keep you glued to your chair in slinking suspense.”
Okay, before we get into it, let’s set the parameters. When moving, we change our position in space. (Duh.) How quickly this position changes and the direction we move is characterized by a term called velocity, which basically means speed plus direction. Lastly, we also think about how quickly velocity changes (and in what direction the change is). This is characterized by acceleration.
Human bodies feel acceleration. We don’t really feel position or velocity. This is because acceleration is directly related to the thing we unquestionably feel: force. If you feel a push or pull, you are being accelerated. If you are falling towards the Earth, you are being accelerated towards the Earth at 9.8 m/s^2. We usually call this number “g”. Then if you experience an acceleration more or less than g, you can use g as an intuitive reference. You can just say 2g for an acceleration twice the size of gravity, which would feel like twice the pulling force. (When referring to forces relative to the force of Earth’s gravity, we sometimes say “g force.”)
Accelerations are produced by changes in speed and changes in direction. We experience 1g all the time. Sharp turns can give more than 1g, and are engineered into amusement park rides. Astronauts train under both low g and high g conditions as these can be physiologically jarring for long durations.
(Important footnotes you might read, especially for the advanced reader:  force experienced and  accelerometer comments.)
Back to Catwoman. Here’s a plot of (total) acceleration vs. time for this ride. The spikes correspond to sudden speed and/or direction changes. Lots of moments of >1g. A pretty fun ride!
I have two traces of data here: the red dots (raw data from the sensor) and the blue line (smoothed data). Having no reference for the accelerometer instrumental error, my feeling is that the blue curve is more trustworthy as far as capturing the overall ride experience. The raw data will contain noise from instrument error as well as noise due to random….um…jiggling.
Which may be significant. Remember where the phone is.
“Spinning around each hairpin curve on a precarious platform of this 3D maze, it really feels like you’re just cutting across the air and could fly off into space at any moment. There are more than 17 sudden turns, twists, dips, or drops built into this wild 1,213-foot track, as you make your way from the top to the bottom of the five-story-tall structure.”
This is a pretty similar ride to Catwoman’s Whip but is literally a much jerkier ride. I don’t just mean this colloquially, as I explain in the next bit, though this is the ride that definitely gave me a bruise or two and made my neck hurt! Kiddie ride my foot! (Though I suppose them kids are a little more…elastic than us olds.)
Recall that acceleration is the rate of change of velocity. Jerk is the rate of change of acceleration, and it’s thought (but not well-understood) to have large physiological effects. Think about it this way: you can get used to a force on you, even if it’s large. You’d adapt to a heavy hat on your head or a weight vest. But you can’t get used to a changing force. So in addition to considering the number of “g’s” a ride provides, a ride engineer will also need to consider jerk. Don’t be a jerk. Here’s a cool (academic) paper on the subject.
“Wicked Cyclone is the first coaster of its kind to have a 200-degree stall and two Zero-G Rolls. You also experience more airtime than any other coaster in New England on Wicked Cyclone.”
Super fun coaster on a wooden track. The acceleration plot gives you an idea of how jerky the ride is. Though since this is a more standard coaster, your neck isn’t being whipped like the Gauntlet ride; you’re mostly keeping forward motion. One of the “zero g” rolls I imagine corresponds to the dip around 60 s, where the acceleration is less than 1 g, though not actually zero as that’s too difficult in practice. (I didn’t have a timer or video device on me, so I would have to repeat this to verify.)
“As you load into your chair, note how there’s something missing here – the floor. You’re about to fight crime with your feet just dangling free in the air. And there’s no track visible above you either – so when you take wing off the first 12-story lift and dive into a 110-foot drop, it will feel just like flying.”
This was a fun one I did more than once! You can see the initial drop is intense and there are plenty of high-g moments. There supposedly is a zero-G roll. Maybe around 95 seconds?
“Welcome to the world of the hypercoaster. This style of roller coaster is so intense they had to come up with a whole new category for it. Hypercoasters are the modern breed of oversized roller coaster that are pumped up to more than 200 feet tall. SUPERMAN The Ride easily clears that distinction, with a height of 208 feet and a mind-blowing 221 foot drop.”
This was the only ride we had to wait in line for. Sadly, there were some track issues so we waited for about 20 minutes, got fed up, and decided to come back later. I got some sweet shots of the creepy test dummies that live under the track though.
After our return, we still had to wait, as it’s the most popular coaster. But only a few minutes. Well worth it! A scary first drop, lots of g’s, and cheesy music the whole time.
Speaking of the first drop, here’s a zoom in on the acceleration. Free fall would be 1.0. But you start at zero at the top so need a finite time to get to 1. It sure feels like free fall though, especially for the second half of the drop! You’re looking straight down too, which adds to the feeling.
“Fly through the air, on this seated, swinging, twisting and tilting carousel that sends you sailing through the clouds.”
This is a swing ride, where you go around in a circle. The primary acceleration comes from the centripetal force caused by rotation. The acceleration can be calculated from acceleration = v^2/R, where v is the speed of the swing and R is the distance from the rotational center. This gives a pretty good acceleration of about 1 g, as you can see from the graph below.
However, depending on how you start the ride, you might also be swinging back and forth, left and right, or some combination, on top of the main circular motion. (Also this ride had some twisting of the swing, too.) The exact kind of swinging is caused by subtle things like how your feet dragged on the ground as you left the ride. This creates the wobbles in the graph. Were there no swinging, we’d expect the graph to be pretty flat.
You can see this effect a little more by looking at the individual acceleration traces. I’m plotting z (red) and x (blue) here. Remember I’m in a moving frame, and the z-axis corresponds to forward/backward. Thus the acceleration in z is roughly zero, but with large fluctuations because I was swinging (I even touched one of my neighboring swings with some regularity). The x-axis is where the main acceleration is, but it’s wiggly too, as I’m shifting in x.
This next plot is just kind of fun, I don’t know what to do with it really. I’ve plotted x, y, and z accelerations “parametrically.” I’ve plotted x vs y, y vs z, and x vs z. I did not filter out the data before the ride began here so there is a big clump at (0,0).
“The New England SkyScreamer and Texas SkyScreamer differ from the standard models, although the actual ride experience is intended to be the same. The gondola of the two rides hold 12 two-seat chairs instead of 16. When the gondola reaches full height, the chairs rotate in a larger circle—124 feet (38 m)—but at a slower speed—35 miles per hour (56 km/h).” – from Wikipedia
This was basically a MUCH higher version of Crime Wave (about 400 ft!). Such awesome views!
We didn’t experience much more acceleration than Crime Wave. Even though its bigger and faster, those effects kind of wash each other out (a = v^2/R). Actually using the numbers above we can calculate expected acceleration! Using these numbers we get about 1.3g. Pretty close to “actual” value seen on graph.
We also have wobble as before. The thing is, no matter the fashion of wobble (left-right or back-forth), every pendulum has natural frequency. We can easily see from the graphs that the period of oscillation is about 8 seconds. Using the formula for period of a pendulum T = 2*pi sqrt(L/g), we can deduce the length of the swing is about 16 meters, about 50 feet. Looking at the still image below this is plausible.
If you take the speed and radius numbers from above, you’ll notice the time for one rotation is also about 8 seconds. My guess is they tune the length of the pendulum so that the period of swinging is close to the period of rotation, so you won’t notice the swinging too much. I didn’t notice any, especially compared to Crime Wave. (Visually, you’ll get about the same view every time around. If the times weren’t matched you’d get a much messier visual stream.) If you noticed the swinging at 400 feet, that would be alarming, though not actually any more dangerous.
And once more, I made the parametric plot of accelerations. No major analysis here, aside from this seems to be a jazzier plot 🙂
Other Rides / Rides We Didn’t Get To
There were a few rides we got to, but my phone wasn’t set up to log data (or I had inadvertently locked the screen while doing the bra maneuver). The Joker was a fun ride, we also did Mind Eraser, and a few others I’m forgetting. After a long and intense day, I didn’t have quite the stamina to take on Goliath. I’m sure it would have been fine, but I decided to save it for next time.
Next time? Well, they were running a special on next season passes, and both of us took them up on it. Also our PocketLabs just came in the mail. 🙂
 I am not getting into details about the rider’s actual experience of the force. I am simply showing the absolute acceleration of the phone, with axes as specified. The actual force experienced by the rider is a whole other calculation in itself, as you need to incorporate the normal force of the seat / restraint, etc. And will also need to take into account non-inertial frames and pseudoforces. Not going there in this post.
 The actual accelerometer technically measures gravity as well, meaning that for someone sitting still, the raw reading will read as 9.8 m/s^2 in whatever direction is vertical. This is because of how accelerometers actually work, as they actually are measuring force and you can’t remove gravity as a force. I am technically using the “virtual accelerometer” which subtracts this effect, and figures out the orientation of the phone/gravity using the gyroscope sensors (I believe). With the virtual accelerometer, no motion –> acceleration reading is zero. As far as I can tell the virtual accelerometer data is trustworthy; it appropriately compensates for orientation. I’ve done a number of controlled motions to test this. The other thing is that especially with the swing rides, there may be some other effects due to misalignment of the sensor. More here.
Are women underrepresented as APS fellows? Yes. Is it anyone’s fault? It’s complicated. Is there anything simple that can be done? YES! Any member of APS can nominate a potential fellow starting early in the calendar year, with deadline varying by unit.
On October 12, APS members received an announcement of newly elected fellows. Fellows are nominated by APS members (anyone can nominate) to a particular unit, are reviewed by a unit selection committee. Those nominations considered deserving of fellowship are then forwarded to the Committee on Fellowship for further consideration, and then recommends the nominees to the APS Council for election. It is my understanding that much of the latter part is a formality; once you are nominated by your unit, you are likely to become a fellow. Units are allocated nominations based on their membership.
When the announcement was made, many were very disappointed about the numbers of women fellows. A large number of units had zero women fellows, and many had only one. In fact, by my count, there appears to only be one unit (DAP) with more than one (3).
As women in physics, we are used to being the minority, so the small numbers in themselves were not (any more) disheartening. What was disheartening was that for several units with (relatively) high women membership, there were zero women fellows. While we are sad and mad, an uncomfortable thought ensues, as we are also scientists:
Is this just a result of small-number statistics?
Think about it this way. You have a six-sided die. You roll the die 5 times. You note the number of times you got a 1. Let’s call this one trial. Do a bunch more trials (5 rolls). You’ll notice that there were many trials that yielded zero 1’s. Maybe more than you would have expected, since you get 5 rolls each time. But there it is.
In this analogy, rolling the die 5 times randomly selects the gender of 5 nominees. Women are #1, men are 2-6. Coincidentally, 1 of 6 is about right for the datasets we’ll consider. Repeat this a bunch of times (different units nominate different sets of 5), you’ll often end up with sets of no women nominees.
So zero women are just likely! Not our fault. ¯\_(ツ)_/¯
But here’s the double-reverse mathe-magic: In a set of 5 random nominees, you might get more zero women sets that you might expect, but you will get even more “enough”/”too many” women sets.
But units are of different sizes and membership compositions…
Indeed. We need to look into the details. I’ve gathered statistics for unit demographics as not all units are equal. (Insert pun about unity here.) I’ve also gathered the information for 2017 fellows by gender and nominating unit. In the case of ambiguous names, I have made sure I researched which gender they appear to present as. (As always, this kind of binary-based methodology ignores the gender spectrum.)
You’ll notice in the demographics that the %men and %women don’t add to 100%. This is because there is a fraction of membership who has left off their gender. This is a large enough fraction it appears to be the result of simply not checking an optional box. So I have assumed this fraction is represented in the same ratio as the %men and %women, to estimate the true percentages. The main issue with these percentages as they stand is that they are for the entire unit membership, and not just for members of the appropriate career stage who are not fellows yet. I don’t have a compelling reason to believe the numbers are all that different, though.
Armed with the percent women and the unit allocations, I proceeded to use the Conference Diversity Distribution Calculator, which I could have written my own code for, but why reinvent the wheel? This tool was developed to demonstrate that statistically speaking, in an unbiased selection, you are (typically) more likely to overrepresent women than have zero women. More on the math here, though if you’ve made it this far, you probably have done this before.
For an input of %women and #nominees, this tool gives me a few numbers. The most likely number of women nominated, the chances that women are overrepresented, exactly represented, and zero. It also provides a ratio of overrepresentation:zero, i.e. the odds of overrepresentation vs none at all.
My question is: in 2017, are there units that are underperforming? Is this larger than the number of units that are overperforming? How do we even define these metrics?
DAMOP has 13.4% women and 11 allocated nominees. They had 1 woman fellow. The most likely number of women fellows is 1 according to the calculator. But this only accounts for about 35% of the possible outcomes. About 20% of the time you get no women, but about 45% of the time you will get more than one woman! However, in terms of “scoring” you might say, DAMOP performed to expectation in 2017.
There were four general groupings that naturally rose once my numbers were in. 1) Larger units that are more likely to overrepresent women compared to zero (cutoff at 1.2). 2) Larger units that are about as likely to overrrepresent compared to zero. 3) Small units that used all of their nominations. 4) Small units that did not use their nominations.
Let’s start with the 3rd group. Basically, these units only had 1 or 2 nominees allocated. All in all 3 of 10 are women. This looks fine.
Let’s move to the 4th group. Basically, these units did not use all of their allocation. This could be an avenue to nominate more women. Generally, things look fine here, though certainly disturbing is the FIAP row (8 nominees, 0 women).
And now we can think a little more concretely, but for funsies, we’ll still go out of order. In group 2, we have about equal odds of overrepresentation vs zero. And yet, the only overrepresentation we see is the trivial one: when most likely = 0 and female fellows = 1. Never do we see most likely =1 and female fellows = 2, even though we should expect to see this fairly often.
I’ve also done a little metascoring. I highlighted the ones with zero women fellows. I also color-coded a bit. If the number of fellows was greater than most likely value = green, equal=blue, less=red. We get three units in the red: GSOFT, DPOLY, DCP. I’m not saying that they generally do bad, but in 2017, they underrepresented women. And it seems reasonable that at least one of those three would have done better, since zero should only happen about 30% of the time. The silver lining is that 4 other units overperformed, albeit trivially.
Group 1 time!
This is where the ugly stuff happens. These are all units with a larger likelihood of overrepresentation than zero representation.
One unit overrepresents women trivially (DPP) with one fellow. But while the most likely value is 0, the overall odds of nonzero are higher than the odds of zero. Only one other unit overrepresents women (DAP) with a most likely value of 2 and an actual value of 3. But even this has a caveat: 60% of the time women should be overrepresented in a sample like this.
DCMP has 1 female fellow of 22. In this case, I’ve lumped 1 or 2 fellows into the “expected” odds of about 43%. However, 52% of the time we should expect n = 3 or greater. A similar issue is seen in DMP.
DGRAV has zero women fellows, but the odds are pretty close to even with a most likely value of zero.
DBIO, DFD, and DNP all have zero, with most likely values of one. It’s unlikely that they would all have the same value, but the numbers tell us it is unlikely (20-25%) for any single one of them to be zero. For all of them to be zero seems especially unlikely.
In summary, while of course any given sample has a significant chance of underrepresenting women, the same sample often has at least an equal chance (if not greater) of OVERrepresenting women. Forget for a moment about the underrepresentation. We should see more overrepresentation if the sampling is unbiased. The pendulum always seems to swing the wrong way.
While I hesitate to suggest anything like quotas, it seems inexcusable that a unit of greater than 5 or so would have zero women fellows in a given year. It’s of course true that any given year there might be zero, but as a pattern over several years, certainly indefensible. But even more so, committees need to not be afraid of (gasp!) nominating more than one woman. Men are already overrepresented. It’s okay if women are overrepresented from time to time.
Who can fix the problem of not enough women nominees? You if you’re an APS member. Nominating committee chairs should also read this document, particularly the last paragraph. Sometimes getting a good pool requires beating the bushes a bit. This is a small numbers game. Just one or two more nominees makes a big difference.
Chad Topaz has a great resource for math (SIAM), I believe one is the works for physics, and will link to it when it happens.
—-A few updates / comments—-
- The corollary: We are clearly not making unbiased selections, as we would do better just by rolling dice! So it’s not helpful to have the illusion that we aren’t biased. However, most of us believe that equitable representation amongst fellows is a worthy goal. The evidence suggests we have to work a little harder than we are to ensure a representative pool.
- Committees, especially those with large allocations, should not be satisfied with one token woman nominee. If for some reason that falls through, then you’re at zero.
- Departments and department chairs should consider nominating candidates from their department. While it might be ideal to have someone from your field nominate you, everyone has too much on their plate already, and can too easily forget about these things. Someone who sees you every day might be the better instigator. (Someone from your field can co-sponsor, as I understand it.)
- I’ve received some comments that the nomination process is burdensome for the nominators, and certainly not just a matter of suggesting a name. Taking a quick glance at it, I don’t disagree. Maybe there is a better system?
It turns out applying and interviewing to faculty jobs and then starting one is a busy time. I’ve just begun my junior sabbatical leave, so now have a bit of bandwidth to write again. It was always my hope to write and do my day job at the same time.
That was a silly, silly idea.
But, I’m hopeful now that I’ve figured things out at work, I will be able to keep this up even once sabbatical is done.
As a fair warning, instead of having different spaces for different things, just about everything is going here. Research paper discussions, science education, personal stuff like trips and fitness, etc…
I will do my best to not embarrass myself and I will also use tags so you can find the content you want.
FYI, most personal/fitness stuff, and anything pre-2017 will be password-protected. If you know me, and want in on these posts, I am happy to send you the password.
At some point I’ll also make a meta-post of with links to all my writing from other sites, news outlets, etc. (i.e. the stuff that had an editor and I sometimes was paid for.)