As a professional philosopher, I am not inclined to believe in curses. However, my experiences over the years have convinced me that I am the victim of what I call the Curse of Springtime. As far as I know, this curse is limited to me and I do not want anyone to have the impression that I regard Springtime Tallahassee in a negative light. Here is the tale of the curse.
For runners, the most important part of Springtime is the Springtime 10K (and now the 5K). Since I moved to Tallahassee in 1993 I have had something bad happen right before or during the race. Some examples: one year I had a horrible sinus infection. Another year I had my first ever muscle pull. Yet another year I was kicking the kickstand of my Yamaha, slipped and fell-thus injuring my back. 2008 saw the most powerful manifestation of the curse.
On the Thursday before the race, my skylight started leaking. So, I (stupidly) went up to fix it. When I was coming down, the ladder shot out from under me. I landed badly and suffered a full quadriceps tendon tear that took me out of running for months. When Springtime rolled around in 2009 I believed that the curse might kill me and I was extra cautious. The curse seemed to have spent most of its energy on that injury, because although the curse did strike, it was minor. But, the curse continued: I would either get sick or injured soon before the race, or suffer and injury during the race. This year, 2017, was no exception. My knees and right foot started bothering me a week before the race and although I rested up and took care of myself, I was unable to run on Thursday. I hobbled through the 10K on Saturday, cursing the curse.
Since I teach critical thinking, I have carefully considered the Curse of Springtime and have found it makes a good example for applying methods of causal reasoning. I started with the obvious, considering that I was falling victim to the classic post hoc, ergo propter hoc (“after this, therefore because of this”). This fallacy occurs when it is uncritically assumed that because B follows A, that A must be the cause of B. To infer just because I always have something bad happen as Springtime arrives that Springtime is causing it would be to fall into this fallacy. To avoid this fallacy, I would need to sort out a possible causal mechanism—mere correlation is not causation.
One thing that might explain some of the injuries and illnesses is the fact that the race occurs at the same time each year. By the time Springtime rolls around, I have been racing hard since January and training hard as well—so it could be that I am always worn out at this time of year. As such, I would be at peak injury and illness vulnerability. On this hypothesis, there is no Curse—I just get worn down at the same time each year because I have the same sort of schedule each year. However, this explanation does not account for all the incidents—as noted above, I have also suffered injuries that had nothing to do with running, such as falls. Also, sometimes I am healthy and injury free before the race, then have something bad happen in the race itself. As such, the challenge is to find an explanation that accounts for all the adverse events.
It is certainly worth considering that while the injuries and illnesses can be explained as noted above, the rest of the incidents are mere coincidences: it just so happens that when I am not otherwise ill or injured, something has happened. While improbable, this is not impossible. That is, it is not beyond the realm of possibility for random things to always happen for the same race year after year.
It is also worth considering that it only seems that there is a curse because I am ignoring the other bad races I have and considering only the bad Springtime races. If I have many bad races each year, it would not be unusual for Springtime to be consistently bad. Fortunately, I have records of all my races and can look at it objectively: while I do have some other bad races, Springtime is unique in that something bad has happened every year. The same is not true of any other races. As such, I do not seem to be falling into a sort of Texas Sharpshooter Fallacy by only considering the Springtime race data and not all my race data.
There is certainly the possibility that the Curse of Springtime is psychological: because I think something bad will happen it becomes a self-fulfilling prophecy. Alternatively, it could be that because I expect something bad to happen, I carefully search for bad things and overestimate their badness, thus falling into the mistake of confirmation bias: Springtime seems cursed because I am actively searching for evidence of the curse and interpreting events in a way that support the curse hypothesis. This is certainly a possibility and perhaps any race could appear cursed if one spent enough effort seeking evidence of an alleged curse. That said, there is no such consistent occurrence of unfortunate events for any other race, even those that I have run every year since I moved here. This inclines me to believe that there is some causal mechanism at play here. Or a curse. But, I am aware of the vagaries of chance and it could simply be an unfortunate set of coincidences that every Springtime since 1994 has seemed cursed. But, perhaps in 2018 everything will go well and I can dismiss my belief in the curse as mere superstition. Unless the curse kills me then. You know, because curse.
At a recent race, a runner entered with a sex of “other” which caused a bit of a problem with the race results. After all, in such competitions people are divided between male and female. They are also divided by age. Because of this, experienced runners tend to check out the competition before the start of the race, looking to see who is present and mentally gauging their chances of being “a have” (runner slang for getting an award).
Since awards tend to be divided into categories of sex and age, runners also try to estimate the age of those they do not recognize. While it is far less common, runners sometimes do need to estimate the sex of the competition. While some people advocate avoiding all concerns about age and sex by only having awards for overall top finishers, there are good reasons to have such categories.
One obvious reason is that awards are intended to increase attendance at the race—people are more inclined to participate when they know they have more chances of winning. If awards were limited to top overall finishers, there would be some decline in participation since people who were not the very top runners would know they had no chance of winning anything.
Another reason is to provide people with a chance to compete in ways that offset advantages. Naturally, almost every race allows people to compete in the overall results, so there is still a very broad competition.
Age has a dramatic negative impact on performance. One major factor is that older athletes do not recover as fast, hence it becomes harder to maintain rigorous training while avoiding injury and being well-rested for the competition. People also get weaker as they age, though diligent maintenance can slow this setting of the sun. Because of this, most races have 5 or 10-year age groups for awards to provide runners with a chance to compete against people with comparable temporal challenges. There are, of course, many older runners can still beat many younger runners, but the general advantage lies with the youth. For most races, runners are on the honor system—they provide their age when they sign up. Some races do, however, require proof of age to avoid people cheating by lying.
While there are female runners who can easily defeat almost any male on the planet in a race, males have various biological advantages when it comes to running, such as greater strength. As such, dividing the awards by sex is a way to account for this difference. There are, of course, some races that do not take this approach, but these are very rare and tend to be small races put on by people not familiar with the usual practices of awards.
As with age, runners are on the honor system in regards to providing their biological sex when they sign up. While a male would generally have an advantage if he could pass a female, this could be challenging given the nature of running attire and various other factors. There are, however, some controversial cases. Perhaps the most famous is that of runner Caster Semenya. Semenya is believed to have an intersex condition which causes the production of high levels of testosterone. High testosterone levels are believed to provide an athletic advantage. It must be noted that while testosterone is associated most with males, females also produce testosterone. In the past, some sporting authorities tested female athletes for high testosterone levels, but this practice has largely changed because female athletes, like male athletes, naturally vary a great deal in their testosterone levels.
While sex-changes are not common, they do occur often enough that the matter has been addressed in sports. Because the division of the sexes in sports is justified on the grounds of relative advantages, females who transition to male can generally compete without restrictions. The easy and obvious justification for this is that such a male would not have any advantage over other males. In fact, they would probably tend to have some disadvantage relative to people who were born male. A male who transitions to female would potentially have an advantage. Because of this, a transitioned athlete need not have surgery, but she is typically required to have undergone at least a year of hormone therapy. This prevents male athletes from simply claiming to be female and competing with an advantage.
There are also people who want to change their gender identification but do not want to undergo surgery or hormone therapy. Some might wonder what would prevent unscrupulous male athletes from gender identifying as females to win races. The easy and obvious answer is that sex divisions in sports are not gender divisions. They are a matter of physical factors and not a matter of social construction. As such, a male athlete who gender identified as a female would still compete against males. They are still a male in regards to the factors that matter in competition.
It could be objected that a person who gender identifies as a man or a woman should be able to compete in accord with their preferred identity. That person might, for example, want their race medal or trophy to reflect this identity—being second female in the 20-24 age group, for example. An easy counter to this is to use an analogy to age—a person might identify as “young at heart” or “and old soul”, but this does not impact their actual chronological age. In the case of athletic competition, this is what matters. If people could pick their age identity for races, this would presumably be used to gain an unfair advantage. So, a 26-year-old person who identified as a 40-year-old would not thus be eligible to win the master’s award (for people 40+).
The next to the last matter to be considered is that which started this discussion; a person who wants to identify as “other.” Resolving this would require determining the basis of the claim of otherness. If the person has a biological identity that falls within established rules for competition (being intersex, for example) then those rules would be applied. If the person has a biological identity that falls outside of the existing rules, then there would seem to be two likely approaches. One would be to match the person with the closest biological sex. The other would be to create a new category for sports and establish standards for being in that category. If the person is electing to select other as a gender identity while having a biological sex, then the person would compete in the category of that biological sex, for the reasons given above.
In closing, there is also a practical matter regarding possible legal troubles. Years ago, I would often see race entry forms with “gender” instead of “sex” because the terms were used interchangeably. These days, “sex” is the standard. If an entry form has “gender” rather than “sex”, then a person could presumably use whatever gender they wish to identify with. This would be rather problematic for the awards budget, since Facebook recognizes over fifty genders. As such, race entry forms should go with “sex.” The form might need to include a brief explanation of the difference between sex and gender to help avoid misunderstandings.
Each of us has their own hill (or mound or even mountain) that is life. I can see the hills of other people. Some are still populated, some still bear the warm footprints of a recently departed fellow runner (goodbye Eric), and so very many of the others are cold with long abandonment. While I can see these other hills, I can only run my own and no one else can run mine. That is how it is, poetry and movies notwithstanding. In truth, we all run alone.
I am in fact and metaphor a distance runner. Running the marathon and even greater distances, gave me a sneak preview of old age. I finished my first marathon at the age of 22, at the peak of my strength, crossing the line in 2:45. Having consulted with old feet at marathons, I knew that the miles would beat me like a piñata—only instead of candy, I would be full of pain. I hobbled along slowly for the next few days—barely able to run. But, being young, I was soon back up to speed, forgetting that brief taste of the cruelty of time. But time never forgets us.
We runners have an obsession with numbers. We record our race times, our training distances and many other things. While everyone is aware that the march of time eventually becomes a slide downhill, runners are thus forced to face the objective quantification of their decline. Though I started running in high school, I did not become a runner until after my first year as a college athlete in 1985 and I only started recording my run data back in 1987. I, with complete faith in my young brain, was sure I would remember my times forever.
My first victory in a 5K was in 1985—I ran an 18:20 for the win. My time improved considerably: I broke 18, then 17 and (if my memory is not a false one) even 16. Then, as must happen, I reached the peak of my running hill and the decline began. I struggled to stay under 17, fought to stay under 18, battled to stay below 19, and then warred to remain below 20. The realization of the damage done by time sunk home when my 5K race pace was the same as the pace for my first marathon. Once, I sailed through 26.2 miles at about a 6:20 per mile pace. Now I have to work hard to do that for a 5K. Another marker was when my 5-mile race time finally became slower than my 10K race time (33 minutes). Damn the numbers.
Each summer, I return to my home town and run the routes of my youth. Back in the day, I would run 16 miles at a 7 minute per mile pace. Now I shuffle along 2 and a half minutes per mile slower. But, dragging all those years will slow a man down. When I run those old routes, I speed up when I hit the coolness of the pine forest—the years momentarily drop away and I feel like a young man again. But, like the deerflies that haunt my run, they soon catch up. Like the deerflies, the years bite. Unlike the deerflies, I cannot just swat them down. Rather, they are swatting me down and, like many a deerfly, I will eventually be crushed and broken by a great hand. In this case, not the hand of some guy from Maine, but the hand of time. Someday, as has happened to friends, I will go out for a run and never come back. But until that day, the run goes on. And on.
“The unquantified life is not worth living.”
While the idea of quantifying one’s life is an old idea, one growing tech trend is the use of devices and apps to quantify the self. As a runner, I started quantifying my running life back in 1987—that is when I started keeping a daily running log. Back then, the smartest wearable was probably a Casio calculator watch, so I kept all my records on paper. In fact, I still do—as a matter of tradition.
I use my running log to track my distance, running route, time, conditions, how I felt during the run, the number of time I have run in the shoes and other data I feel like noting at the time. I also keep a race log and a log of my yearly mileage. So, like Ben Franklin, I was quantifying before it became cool. Like Ben, I have found this rather useful—looking at my records allows me to form hypotheses regarding what factors contribute to injury (high mileage, hill work and lots of racing) and what results in better race times (rest and speed work). As such, I am sold on the value of quantification—at least in running.
In addition to my ORD (Obsessive Running/Racing Disorder) I am also a nerdcore gamer—I started with the original D&D basic set and still have shelves (and now hard drive space) devoted to games. In the sort of games I play the most, such as Pathfinder, Call of Cthulu and World of Warcraft the characters are fully quantified. That is, the character is a set of stats such as strength, constitution, dexterity, hit points, and sanity. Such games also feature sets of rules for the effects of the numbers as well as clear optimization paths. Given this background in gaming, it is not surprising that I see the quantified self as an attempt by a person to create, in effect, a character sheet for herself. That is, to see all her stats and to look for ways to optimize this character that is a model of the self. As such, I get the appeal. Naturally, as a philosopher I do have some concerns about the quantified self and how that relates to the qualities of life—but that is a matter for another time. For now, I will focus on a brief critical look at the quantified self.
Two obvious concerns about the quantified data regarding the self (or whatever is being measured) are questions regarding the accuracy of the data and questions regarding the usefulness of the data. To use an obvious example about accuracy, there is the question of how well a wearable really measures sleep. In regards to usefulness, I wonder what I would garner from knowing how long I chew my food or the frequency of my urination.
The accuracy of the data is primarily a technical or engineering problem. As such, accuracy problems can be addressed with improvements in the hardware and software. Of course, until the data is known to be reasonably accurate, then it should be regarded with due skepticism.
The usefulness of the data is partially a subjective matter. That is, what counts as useful data will vary from person to person based on their needs and goals. For example, knowing how many steps I have taken at work is probably not useful data for me—since I run about 60 miles per week, that little amount of walking is most likely insignificant in regards to my fitness. However, someone who has no other exercise might find such data very useful. As might be suspected, it is easy to be buried under an avalanche of data and a serious challenge for anyone who wants to make use of the slew of apps and devices is to sort out the data that would actually be useful from the thousands or millions of data bits that would not be useful.
Another area of obvious concern is the reasoning applied to the data. Some devices and apps supply raw data, such as miles run or average heartrate. Others purport to offer an analysis of the data—that is, to engage in automated reasoning regarding the data. In any case, the user will need to engage in some form of reasoning to use the data.
In philosophy, the two main basic tools in regards to personal causal reasoning are derived from Mill’s classic methods. One method is commonly known as the method of agreement (or common thread reasoning). Using this method involves considering an effect (such as poor sleep or a knee injury) that has occurred multiple times (at least twice). The basic idea is to consider the factor or factors that are present each time the effect occurs and to sort through them to find the likely cause (or causes). For example, a runner might find that all her knee issues follow times when she takes up extensive hill work, thus suggesting the hill work as a causal factor.
The second method is commonly known as the method of difference. Using this method requires at least two situations: one in which the effect in question has occurred and one in which it has not. The reasoning process involves considering the differences between the two situations and sorting out which factor (or factors) is the likely cause. For example, a runner might find that when he does well in a race, he always gets plenty of rest the week before. When he does poorly, he is always poorly rested due to lack of sleep. This would indicate that there is a connection between the rest and race performance.
There are, of course, many classic causal fallacies that serve as traps for such reasoning. One of the best known is post hoc, ergo propter hoc (after this, therefore because of this). This fallacy occurs when it is inferred that A causes B simply because A is followed by B. For example, a person might note that her device showed that she walked more stairs during the week before doing well at a 5K and simply infer that walking more stairs caused her to run better. There could be a connection, but it would take more evidence to support that conclusion.
Other causal reasoning errors include the aptly named ignoring a common cause (thinking that A must cause B without considering that A and B might both be the effects of C), ignoring the possibility of coincidence (thinking A causes B without considering that it is merely coincidence) and reversing causation (taking A to cause B without considering that B might have caused A). There are, of course, the various sayings that warn about poor causal thinking, such as “correlation is not causation” and these tend to correlate with named errors in causal reasoning.
People obviously vary in their ability to engage in causal reasoning and this would also apply to the design of the various apps and devices that purport to inform their users about the data they gather. Obviously, the better a person is at philosophical (in this case causal) reasoning, the better she will be able to use the data.
The takeaway, then, is that there are at least three important considerations regarding the quantification of the self in regards to the data. These are the accuracy of the data, the usefulness of the data, and the quality of the reasoning (be it automated or done by the person) applied to the data.
Running with the Pack: Thoughts from the Road on Meaning and Mortality
Mark Rowlands (Author) $25.95 November 2013
Like Mark Rowlands, I am a runner, a known associate of canines, and a philosopher in Florida. This probably makes me either well qualified as a reviewer or hopelessly biased.
While the book centers on the intrinsic value of running, it also addresses the broader topics of moral value and the meaning of life. While Rowlands references current theories of evolutionary biology, he is engaging in philosophy of the oldest school—the profound and difficult struggle to grasp the Good.
Decisively avoiding the punishing style that often infects contemporary philosophy, Rowlands’ well-crafted tale invites the reader into his thoughts and reflections. While Rowlands runs with canines rather than his fellow “big arsed apes” his writing has the pleasant feel of the well-told running story. While the tale covers a span of decades, it is nicely tied together by his account of his first marathon.
Since the book is about running and philosophy, there is the question of whether or not the book is too philosophical for runners and too “runsophical” for philosophers. Fortunately, Rowlands clearly presents the philosophical aspects of the work in a way that steers nicely between the rocks of being too technical for non-philosophers and being too simplistic for philosophers. As such, non-philosophers and philosophers should find the philosophical aspects both comprehensible and interesting.
In regards to the running part, Rowlands takes a similar approach: those who know little of running are provided with the needed context while Rowlands’s skill ensures that he still captures the attention of veteran runners. This approach ensures that those poor souls who are unfamiliar with both running and philosophy will still find the book approachable and comprehensible.
While the narrative centers on running, the book is a run across the fields of value and the hills of meaning. In addition to these broad themes, Rowlands presents what seems to be the inevitable non-American’s critique of American values. However, Rowlands’s critique of American values (especially our specific brand of instrumentalism) is a friend’s critique: someone who really likes us, but is worried about some of our values and choices. Lest anyone think that Rowlands is solely critiquing America, his general concern is with the contemporary view of value as being purely instrumental. Against this view he endeavors to argue for intrinsic value. Not surprisingly, he claims that running has intrinsic value in addition to its obvious instrumental value. While this claim generally seems self-evident to runners, in the context of philosophy it must be proven and Rowlands sets out to do just that.
Interestingly, he begins with a little known paper by Moritz Schlick in which he contends that play has intrinsic value. He then moves to Bernard Suits’s account of what it is to be game and notes that running is a form of play; that is, it involves picking an inefficient means of achieving a goal for the sake of engaging in the activity. Running is not a efficient way of getting around in an age of cars, but runners often run for the sake of running-thus running can be a game.
As Rowlands tells the reader, his approach is not strictly linear and he takes interesting, but relevant, side trips into such matters as the nature of the self and of love. These side trips are rather like going off the main trail in a run—but, of course, one is really still on the run.
Near the end of this run, Rowlands goes back to the origins of philosophy in ancient Greece. He notes that the gods, such as Zeus, showed us that play is an essential part of what is best. The philosophers showed us that the most important thing is to love the good. The athletes taught us that running is play and therefore has intrinsic value.
He ends his run with a discussion of joy, which is the recognition of things with intrinsic value. As he says, dogs and children understand joy but when we become adults we lose our understanding—but this need not be a permanent loss.
While Rowlands’s case is well reasoned, he does face the serious challenge of establishing intrinsic value within the context of what I call the MEM (mechanistic, evolutionary, and materialist) world. Many ancient (and later) philosophers unashamedly helped themselves to teleological and metaphysical foundations for the Good. While this generated problems, this approach could seemingly ground intrinsic value. While I agree with Rowlands’s conclusion, I am in less agreement with his attempt to establish intrinsic value in his chosen world view. But, it is a good run and I respect that.
Like a long run, Rowlands’ book covers a great deal of ground. Also like a long run, it is well worth finishing. Plus there are dogs (the most philosophical of animals).