2020 Challenge

My interest in boardgames revived in the ‘90s, while I was in graduate school. What we called German games (later christened eurogames) invaded the U.S., the charge led by Settlers of Catan. I still have my battered first-edition copy, but the last time I cracked it open was almost a decade ago.

Back then, I scoured the nascent internet for scraps of information on the latest games, carefully mulling purchases on my grad student budget. The birth of the BoardGameGeek, today’s gargantuan information clearing house, was born twenty years ago this month. It was small then, and just one of several websites I perused, almost all of which are now defunct. I’ve had a resurgent interest in boardgames this past month, probably fleeting, as my game-playing has seen a steady decline the last decade as I’ve moved on to other interests.

In any case, this week I’ve been reading about various 2020 Challenges. There are several versions, but a common one this decade has been the “10 x 10”: Designate 10 boardgames you plan to play at least 10 times in the upcoming year; then challenge yourself to accomplish this! As a long-time boardgamer, I see the appeal. Over time, one’s collection grows, and fewer games are played multiple times; many languish on the shelf on in storage boxes.

There were other challenges back in the day. I started logging my game plays in 2003 based on a popular gaming challenge to play 365 games per year; these did not have to be unique games – the idea was to average at least one game per day. In the early years I succeeded, but more recently I have not. Then there were the Five & Dime lists where you noted games you played at least five or ten times at the end of the year. I participated in some of those; my record has been thinning over the years as shown in the graph below.

In my gaming heyday, I would easily have met the 10 x 10 challenge – although this is hindsight since I had not designated the challenge games ahead of time. However, the past decade, I’ve only managed an average of five games played at least five or ten times. The stalwart favorites are Race for the Galaxy, 10 Days in Europe/Asia, Lost Cities, Chrononauts, Mystery Rummy #1. All shorter, quicker games. Occasionally a new game would receive lots of play, as when I play-tested Bios Genesis extensively in 2016. But typically longer games might go un-played for years. I started thinning out my collection the past three years based on these statistics. For the past decade, if not for Race for the Galaxy, I would be playing half the number of games. The chart below shows total number of games (orange) and the proportion (yellow) that were Race for the Galaxy (starting 2008).

It seems to me that a 2020 Challenge should be a 20 x 20, but this is doubly difficult. I’m not sure I could even accomplish a 10 x 10. But maybe I can come up with an aspirational list – games that haven’t been played in a while that I remember greatly enjoying. Perhaps that will motivate me to bring them off the shelf, or introduce them to a new generation. Thus, in no particular order, here are ten games I would like to revisit.

War of the Ring

Robo Rally

Valley of the Mammoths

Tikal

Factory Fun

History of the World

Puerto Rico

Vegas Showdown

Through the Desert

Saint Petersburg

Cheers to 2020!

On Poetry

I became a Philistine in my twenties. Partly it’s because that’s when I entered college and discovered my love of science. Chemistry tickled my brain in a sublimely satisfying way. I enjoyed reading ahead in my textbook, working on problems, and yes, even taking exams. Studying no longer felt like a chore. I stopped reading fiction, thinking it child’s play. I never understood poetry, thinking it frivolous and bombastic. Science and technology was the way of the future – ideas and imagination, yet grounded in reality. I was definitely a Wizard, not a Prophet.

Let me back up.

I grew up in a country with a (then) rigid school system with no choices in class schedules and subjects. National exam results shunted students along pre-determined pathways; mine happened to steer me towards the sciences in high school, but I never really understood nor loved the subject material. National exams were a chore, a rite of passage to be endured, and like many of my fellow classmates, I waited until the last minute to cram for the exam. I enjoyed school. But not for the learning. I enjoyed my time outside of school even more. Playing with friends. Reading fiction. Enjoying Choose-Your-Own-Adventures. I also enjoyed reading non-fiction, but made no distinction between different genres. Library access was limited, so I devoured everything I could get my hands on. However, I had read no poetry beyond childhood nursery rhymes.

College allowed me to choose my classes for the first time. This was bewildering, especially since I chose to attend a liberal arts college in the U.S. that I knew nothing about, beyond the very nice brochure and the generous financial aid package. I took solely science and math courses my first year, because I didn’t know any better. It’s when I discovered my love of chemistry, for which I’m very thankful! But I had to fulfil the distribution breadth requirements of my liberal arts education. In my second year, I chose philosophy and psychology to cover those bases while taking every chemistry class I could fit into my schedule. I avoided any class that smacked of Literature, not that I knew what it was. The required “freshman” humanities class was delayed until my final year, and to be honest, I skipped class aplenty while working on my chemistry senior thesis.

In my thirties, starting as a faculty member at a liberal arts college, I began to broaden my scope – at least enough, to be a competent academic adviser. I listened sympathetically to students agonizing over their schedule choices, especially since many of them had much broader interests than I did when entering college. I educated myself on the value of different majors in areas I was unfamiliar – the humanities and social sciences. I prepared to answer questions about the value of a liberal arts education, although I suspect my answers were simplistic in those early days. Several of my chemistry student advisees told me how much they enjoyed their sophomore-level poetry class offered through the English department; I made a mental note of it as an academic adviser.

Since then I’ve returned to the broader reading diet of my childhood. My choices are abundant and I’ve learned to be picky; I’ve blogged about a number of those books (but not all). This past year, there’s been a resurgence of sci-fi/fantasy; I recently finished the Broken Earth series and the trilogy that began with The Three-Body Problem. I think (or at least hope) I’ve been thoughtful about How-To-Read different genres. Yet I’ve managed to avoid poetry all these years.

But on holiday this week, a book caught my eye: How to Read Slowly by James Sire. I’d read the Mortimer Adler classic which has especially shaped how I read fiction. Browsing  through Sire’s book, I was reminded of thoughtful habits to get the most out of my reading – be it information or pleasure. I considered skipping the chapter on poetry, but I’m glad I persevered. Sire cracked open what to me was mystifying – why anyone actually enjoyed poetry. I’ve read poems before, recommended by friends, but found them obtuse and boring. That’s because I did not read slowly enough, and didn’t understand how to approach poetry.

Sire describes poetry as “Catching Reality in a Mirror”. Here are two paragraphs from his description.

About the great events of life prose seems and inadequate means of expression. Love, death, fate, the capture of a city, the defeat of an archenemy: to reflect on these with depth is to engage, if only internally, in a poetic monolog. Whenever one feels deeply, whenever mere words seem inadequate to capture the quality of a moment or an idea, we turn to poetry, either by writing it or searching for the right poem to read and meditate on.

In great literature – poetry and fiction – we see ourselves, our friends, our enemies, the world around us. We see our interests portrayed in bold relief – our questions asked better than we can ask them, our problems pictured better than we can picture them by ourselves, our fantasies realized beyond our fondest dreams, our fears confirmed in horrors more horrible than our nightmares, our hopes fulfilled past our ability to yearn or desire.

But Sire does more than pontificate about the joys of poetry. He guides the reader through several different types of poems, pointing out the interesting unique features of each piece. Good poems, great poems, are carefully constructed and multi-layered. A single quick read will never do. I should have known better. I tell my students that when they read primary literature they won’t understand it the first time. Usually it’s because they don’t have the experience and they trip over the dense, technical language. But things become a little clearer on a second or third read. The same is true for poetry.

In playful paragraphs, Sire exhorts the reader to re-read the poem after he reveals each morsel of analysis. Normally I wouldn’t bother, but I was on holiday, what the heck? Maybe there’s something to this mumbo-jumbo. Then lo and behold, I was very surprised at how it works! I can’t quite describe the delightful surprise I encountered on a second reading, on a third reading. Read aloud, Sire exhorts. I did so – and again I was surprised at the difference it makes. It’s like a light turning on – illuminating the poem. It’s deeper and more interesting than I would have imagined. I now think reading poetry might be both pleasurable and informative. But I have to read slowly, allowing the colorful language and rhythm to sink in according to its own timing.

Reading Sire’s chapter on poetry shed light on something I’d been pondering recently – Is it possible to read nonlinearly? I don’t mean jumping pages in a Choose-Your-Own-Adventure; that’s still a linear storyline. I’m thinking of whether it’s possible to read multi-linearly, through several different channels at once. The initial conundrum comes from my origin-of-life reading: Why is information linearly encoded in DNA/RNA and proteins? There are other branched molecules, oligomers, and polymers, in biochemistry – but the main so-called information-carrying molecules are linear chains.

Think about how we read. Symbols on a page. One after another. Linear. Maybe left-to-right, right-to-left, top-to-bottom, but still linear. Hypertext with branching links? You might zig and zag, but it’s still one-dimensional. Is it because our brain is a linear processor at heart? Action potentials flow one-way. But neurons are arranged in an intricate web, and there’s an interesting interplay between the digital and the analog in the way information is received and responded to in our brains.

We can process information through multiple channels. I’m sitting in front of a TV screen with subtitles, with the volume on, and I understand both languages. I’m watching, reading and listening, simultaneously. 3D-TV. If a next-gen TV released some olfactory molecules, a fourth channel would open for my sense of smell. 4D-TV. The information in each strand might be impinging upon me linearly, but I’m processing in parallel. Multi-linear. It’s a richer experience. Perhaps more efficient, kinda like slime mold quorum sensing.

Our bodies and minds are taking in so much more information than our conscious awareness processes. To get a glimpse of the richness, we could attend to different aspects of the multi-linear information stream. Our friends could help us by co-experiencing an event providing an even richer smorgasbord, as each person notices different sights, smells and sounds.

Poetry does something similar, yet different. The medium might simply be words, but the words have been thoughtfully arranged for a multi-linear experience – one that requires multiple readings, voiced readings, slow readings. It might be the closest thing we can do to  communicate depth-of-experience where mere non-fictive description simply will not suffice.

Geoarcanity

Every season comes to an end. In geology, eons are separated by physical markers. In our planet’s story there was the Hadean, then the Archean, then the Proterozoic, and we now live in the Phanerozoic – the era of visible life. The marker of what’s visible is anthropocentric since we’re the species doing the naming. In history, eras are marked and demarcated by events deemed important to humankind. Pre-history was the time before humans showed up. Anthropocentric, of course.

But once humankind gained the power to terraform its own planet, geology and history coincide. The former operates on long time scales, hard to imagine by humans, and seemingly independent of our needs and wants. The latter is the quick blink of an eye in what has been a long saga, although we often portray it as the story’s apex. In The Stone Sky, a fitting conclusion to the Broken Earth trilogy that began with The Fifth Season, the cosmic and the present sliver of time are juxtaposed. The more some things change, the more they remain the same. Can the cycle be broken?

The dynamics of life – biochemical life from my point of view as a chemist – is about energy transduction. Collecting energy. Moving energy. Dissipating energy. These seem like separate activities, but their boundaries blur as you delve deep, much like the artificial demarcations of geology and history. Seemingly blind chemical forces do this. Seemingly not-so-blind human history does the same; the digital ecosystem today is a growing beast, hungry in its energy consumption. A not-so-ecological ecosystem perhaps. The seemingly technological ‘progress’ of humanity runs on better ways to extract and use energy. Most of our energy is utilized from the sun; but the active geochemistry of our planet provides an alternate source.

We’ve been manipulating energy throughout history, but we don’t understand its fundamental essence. Try to define energy, and it dynamically slips out of our static strictures. We don’t know what it is. But we can count it. At least, that’s what I tell my students on the first day of second-semester general chemistry. N. K. Jemisin does a masterful job tackling these themes in The Stone Sky in three illuminating, yet cryptic, paragraphs (in italics below).

All energy is the same, through its different states and names. Movement creates heat which is also light that waves like sound which tightens or loosens the atomic bonds of crystal as they hum with strong and weak forces. In mirroring resonance with all of this is magic, the radiant emission of life and death.

This is our role: To weave together those disparate energies. To manipulate and mitigate and, through the prism of our awareness, produce a singular force that cannot be denied. To make of cacophony, symphony. The great machine called the Plutonic Engine is the instrument. We are its tuners.

And this is the goal: Geoarcanity. Geoarcanity seeks to establish an energetic cycle of infinite efficiency. If we are successful, the world will never know want or strife again… or so we are told. The conductors explain little beyond what we must know to fulfil our roles. It is enough to know that we… will set humanity on a new path toward an unimaginably bright future.

In Jemisin’s telling of the tale, the quest for energy is Geoarcanity. I like her use of the prefix Geo because it encompasses more than rocks or solid earth. The geosphere comprises the lithosphere, hydrosphere, atmosphere, and biosphere. All teeming with energy! But mysterious. Arcane. Geoarcanity is modern technological humankind’s Tower of Babel dream, perpetual motion machines to cold fusion notwithstanding. We’re building better energy extractors, better energy storage units, more efficient devices. Our technogizmos are becoming more compact and it’s amazing what they can do!

This is magic, after all, not science. There will always be parts of it that no one can fathom… Put enough magic into something nonliving and it becomes alive. Put enough lives into a storage matrix, and they retain a collective will, of sorts.

Jemisin intricately weaves energy and magic, and presents an intriguing hypothesis. If magic is the dynamic of life, a sufficient compaction of the ‘stuff’ will turn non-living matter into something we would characterize as life. Magic is ill-defined, as is energy. Geoarcanity attempts to concentrate this ingredient into a matrix – giving the planet its seemingly inexhaustible supply of energy. There are echoes of The Matrix in the dystopian tale. History repeats itself. Revolutions. A new season begins.

Grade Inflation

Where to begin?

Let’s look at some data. The figures below are from “Where A is Ordinary: The Evolution of American College and University Grading, 1940-2009” (Rojstaczer and Healy, Teacher’s College Record, 2012, 114, 070306). The dataset is large and comprises a variety of institutional types, both private and public, and also by region.

The most dramatic trends are the increase of A’s and the decrease of C’s. There’s a dip in D’s but smaller. B’s hover between 35-40% and F’s hold steady at 5%. C was the most common grade circa 1940 but was supplanted by B starting in 1965. In 1997, A became the most common grade and its rise continues. (The small “bump” in A’s in the early ‘70s is partly attributed to instructor attitudes during the Vietnam war.) There are differences in the fine-grained data. Schools in the South tend to give lower grades. Public schools show less inflation than privates. Some specific schools (Central Michigan, Colorado, Princeton) had specific policies to combat grade inflation. The private-public difference is the starkest as seen from data comparing 1960, 1980, and 2007.

Why are grades rising?

Some schools argue that they are getting academically stronger students. Perhaps they are, at least based on increases in average SAT/ACT scores of incoming students. However, this increase is small percentage-wise compared to the increase in grades. Some argue that more women attending college have moved grades up. (It is well documented that on average women perform better GPA-wise.) However, comparing both the period and the rate of increase in percentage of college-attending women once again does not match up with observed grade inflation. Perhaps teaching methods are improving. But there’s little data to suggest any effect even if the statement is (partially) true. Perhaps students are choosing “easier” majors. Perhaps the rise in adjunct faculty with precarious job security. Perhaps the changing attitude of higher education towards student-as-customer.

These and other scenarios are discussed by Rojstaczer and Healy with no firm answer to the why question, although they seem to favor the last “perhaps” in the paragraph above when writing their conclusion: “Our data suggest that in the absence of oversight from leadership concerned about grade inflation, grades will almost always rise in an academic environment where professors sense that there are incentives to please students… the principle incentives likely have been a mix of the ability to enhance students’ postgraduate prospects, and with the rise of importance of student-based evaluations, faculty self interest.”

The problem stated by the article title remains. Once an A becomes ordinary, it loses value as a signaling factor to employers, post-graduate programs, and any other entity using grades as some sort of benchmark. It is not surprising to see the rise of alternate credentialism. Rojstaczer and Healy exhort universities to rein in “grades that bear little relation to actual performance”. This will not be easy, because of larger systemic issues. The issue is complicated, although economists (particularly game theorists) are attempting to model “what if” scenarios to get at the problem. The remainder of today’s blog post will highlight just a few examples. Note that unlike the data-driven model for grade norming previously discussed, most game-theory studies come with necessarily simplified assumptions without a supporting data set. I won’t go into the game-theory models, but I will cite the papers for the math-inclined who’d want to read propositions, proofs, and lemmas.

In “A Signaling Theory of Grade Inflation” by Chan et al. (International Economic Review, 2007, 48, 1065-1090), their game-theory model suggests that when one school inflates grades, other schools will follow suit. Here’s the crux: “When a school gives a lot of good grades, the labor market cannot fully distinguish whether this is due to an overly liberal grading standard or whether the school is blessed with a large proportion of high-ability students.” On the one hand, schools trying to help their students get jobs have an incentive to raise grades. On the other hand, a school’s top students might be less distinguishable from their peers. Employers have some knowledge of a student’s abilities, but less than the school that’s (purposely/strategically) providing coarse-grained signaling.

Essentially, “grade inflation reduces the information content of grades and thus impedes optimal task assignment by employers”. To hedge their bets, employers might choose to hire higher GPA students from colleges that have higher average GPAs because there’s a decent probability of getting a high-ability student. Elite colleges are incentivized to continue grade inflation to keep more of their students in these “virtuous cycles”, and thus the hamster wheels turn. After all, brand-name is a signaling factor. Those of us in academia know this well, unfortunately. Academic snobbery is rife. I’m sure I benefited, at least getting my foot in the door where job offers were concerned. But grade inflation isn’t purely an academia issue; similar phenomena show up in consumer magazines, accounting audits of companies, stock-picking in investment banking, and more. Hence, the interest of economists, I guess.

Schools that perpetrate more grade inflation, might, they just might, have better quality education – at least judged by increased resources funneled into education. That’s the argument by Boleslavsky and Cotton in “Grading Standards and Education Quality” (American Economic Journal: Microeconomics, 2005, 7, 248-279). The situation faced by employers (or evaluators): “transcripts are less informative, hindering the ex post selection of high-ability graduates, but schools invest more in education quality, so graduates are more likely to be high ability ex ante.” The authors’ model finds that this “effect on investment may dominate” setting up a complementarity between school and employer. A look at the U.S. landscape certainly bears this out when considering the brand-name schools with large endowments and operating expenses. A virtuous upward spiral perhaps.

The final article I highlight today is “Student sorting and implications for grade inflation” by Herron and Markovich (Rationality and Society, 2017, 29, 355-386). In their model, departments (majors) are divided into two types: ability-revealing or ability concealing. Their conclusion: grade inflation is (at least partly) driven by how students sort themselves into departments. This grade-related sorting was mentioned in my previous post as to why grade norming might bring more women into STEM. The authors sound a cautionary note to a hypothetical university dean who sees many A’s in a department. In an ability-concealing department, grades aren’t distinguishing the stronger students from the less academically-inclined. In an ability-revealing department, avoided by weaker students, the high grades might be an accurate reflection of student caliber. Thus, the claim “our students are excellent!” might be true and reasonable. How might one tell the difference? The authors suggest indirect measures (because there are confounding variables); I encourage you to read the article for details. There is evidence that introductory course grades in ability-revealing departments (where you see the full distribution of grades including many low ones) are better predictors of future student performance comparted to ability-concealing departments.

There are several interesting offshoots mentioned at the end of the article. The authors make an argument that the emergence of ability-concealing departments in a university, even just one or two, could over time lead to the present situation because of the way students sort themselves based on their own perceived abilities. Weaker students move towards ability-concealing departments, while stronger students, to distinguish themselves, migrate to ability-revealing departments. The (idealized) model assumes students know their own ability and can perfectly rank themselves within their cohort. This, we all know as educators, is not true – thus the actual situation is much messier. The authors write: “This sort of error is particularly pernicious for students because a high-ability student who believes that she is of low ability may prefer an ability-concealing department over an ability-revealing department – even though the latter would be more valuable.” Risk-aversion is complicated, although studies do show that on average women are more risk averse than men, at least where grades are concerned.

What to do?

I close by quoting the authors on the challenges of a department trying to mitigate grade inflation. “A department that by itself wanted to address institution-wide grade inflation can be stymied by the ability-concealing behaviors of other departments. If an ability-revealing department were to make its classes increasingly challenging in an attempt to mitigate inflation, then it would make the overall grade inflation problem worse and in so doing decrease its own enrollments. To the extent that low enrollments are problematic for departments are problematic for departments who might want to use enrollment figures to argue for faculty positions, no department has an incentive on its own to increase the cost associated with its classes. This sort of collective action dilemma means that university administrators should not assume that individual departments will ever be able to coordinate themselves and form a solution to what administrators might consider a grade inflation problem.”

Grade Norming

“I grade on an absolute scale. There is no curve. This means that potentially everyone in the class could get an A. This is unlikely to happen. It also means that potentially everyone could fail. This is even less likely.”

That’s what I tell my students on the first day of class, in every single one of my classes – because the law of small numbers applies when one teaches in a liberal arts college with small-ish class sizes. My students know what this absolute scale is score-wise because it’s explicitly stated in the syllabus. Mostly, they are heartened by there not being a curve; they likely focus on the part where they might score A’s and not where they might fail. The reality at semester’s end is, in my intro-level chemistry courses, something resembling a normal distribution (with fatter tails). The mean and median grades depend on the size of the class and average student interest and ability. (In physical chemistry, the distribution is often bimodal.)

Larger institutions with huge introductory lecture courses might grade on a curve. In other countries, professors may not have the final say on the final grades because the university administration may impose grade norming. This sounds like anathema to faculty at U.S. institutions, but the issue is more complex than at first glance. I, for one, have no plans to change my grading policies. (I tell students I don’t assign their grades; rather students earn their grades.) That being said, it’s not because I’m absolutist. Rather I’m in a system that gives faculty autonomy over grades and I have particular ideas as to what constitutes A work, B work, C work, and so on. Also, the law of small numbers. (For more on the purpose of final grades, see here.)

All the above is to preface the hullaballoo this week in higher education circles from a white paper published by the National Bureau of Economic Research (NBER). The somewhat cryptic title is “Equilibrium Grade Inflation with Implications for Female Interest in STEM Majors”. I wouldn’t have noticed it if not for the InsideHigherEd (IHE) article “Grading for STEM Equity”, with the provocative lede:

Study suggests that professors should standardize their grading curves, saying it’s an efficient way to boost women’s enrollment in STEM.

That definitely catches eyeballs, particular since the article opens with:

Harsher grading policies in [STEM] courses disproportionately affect women – because women value good grades significantly more than men do according to [NBER paper]. What to do? The study’s authors suggest restricting grading policies that equalize average grades across classes, such as curving all courses around a B grade. Beyond helping close STEM’s gender gap, they wrote, such a policy change would boost overall enrollment in STEM classes.

The IHE article is short, yet does a good job summarizing the main research points. Cue the extensive comments section; some thoughtful, others clearly indicating they have not read the NBER paper (and skimmed the IHE article too quickly), none of which is surprising.

The paper itself is quite interesting. The suggestion of grade norm(aliz)ing around a B average comes from building an economic model based on extensive data from the University of Kentucky, and then applying counterfactuals to examine how students might sort themselves differently into majors and their associated classes. One can quibble with the model parameters, for example, I thought the professor utility function they applied was much too simplistic, but overall I felt that they had reasonable justification for their model (from my non-expert point of view). I recommend reading the paper if you’re interested in the details. (I read the actual NBER Dec 2019 article but if you’re trying to avoid a paywall, searching the article title will reveal earlier working copies that are somewhat close to the final version.)

The data is interesting. Unsurprising was that STEM classes were associated with lower grades. (The authors grouped Economics, Finance, Accounting, and Data Sciences with the standard STEM areas.) Average grades were 2.94 and 3.27 for STEM and non-STEM respectively. For women, these averages were 3.00 and 3.37, i.e., women score better than men in both STEM and non-STEM. One compounding factor is that STEM classes were on average twice as large as non-STEM classes (80 versus 40) – likely due to those large intro STEM classes. Also unsurprising was that self-reported outside-of-class study time was 40% higher in STEM classes. More shocking were the actual average numbers of 3.37 and 2.45 hrs/week for STEM and non-STEM respectively. That’s very low! While self-reporting is always suspect, that’s 20,000 students and the over and under-estimations might cancel out. We also know from other longitudinal studies that study hours have decreased steadily over the years and the U of K numbers are not out-of-whack for the present decade.

Looking at details more closely, larger classes do indeed show inverse correlation with grades. Classes with more women have higher average grades. Classes with more women have higher study hours. And then the kicker: Classes with higher grades show less self-reported study time. The authors note that “grade inflation may have negative consequences for learning.”

The meat of the NBER paper is the model they build whereby “grading policies influence enrollment decisions directly because students value grades but also indirectly through incentivizing (costly) study effort.” Each course is assigned a payoff based on a student’s preference for the course, how much time he/she is willing to study, and an expected grade based on such effort. Students sort themselves into courses and receive potential grades that depend on academic preparation, study effort, professor “grading policy”, among other things. There’s a bunch of math and the model is parameterized.

Some interesting things that come out of the model: Women study a third more than men. Doubling study effort leads to larger grade increases in STEM versus non-STEM; the extremes are Engineering (0.37 grade increase) and Management & Marketing (0.13 grade increase). There’s a likely-to-be-controversial table showing the ability weights of women being lower in STEM areas, with Chemistry & Physics at the bottom of the pack. Expected GPAs for both women and men are also lowest in Chemistry & Physics (and lower in STEM overall), however, interestingly, stronger students tend to sort towards STEM. This is not because men are necessarily better; women still earn higher grades in STEM, but they also earn higher grades in non-STEM and tend to flock there. Women study more regardless.

The modeling of professor preferences is interesting. STEM professors prefer lower average grades and higher workloads than non-STEM. Hmm… I wonder if that’s true of me compared to my non-science colleagues. The model also suggests that “both STEM and non-STEM professors prefer to give out higher grades with lower workloads in upper-division classes.” Hmm… that’s definitely not true for me workload-wise because my standard upper-division class is Physical Chemistry – considered the hardest and least liked by our majors. I might prefer to give higher grades, but I don’t actually end up doing so. The average grade in my P-Chem classes is slightly lower than in my G-Chem classes, but not by much. The model assumes professors prefer smaller classes (true, I think), but the weighting factor in the model leads to lower grades in STEM classes even though there is higher demand from students. That’s eerie. I don’t think I subconsciously give lower grades in larger classes – students earn their grades! – but I don’t disagree with the trend. I see it in my own classes. I’d like to think it’s because I’m more effective at helping a larger proportion of individual students (who need the extra help) in a smaller class. Time taken up by students in office hours doesn’t change substantially with class size (but it does change a little).

After building and parameterizing their model, the researchers can start testing counterfactuals and examining how this affects the so-called STEM gap – that women disproportionately choose non-STEM areas. The three largest factors that narrow the gap are equalizing non-grade preferences, equalizing grade preferences, and grade norming around a B average. There isn’t much an institution can do about the first two areas. While much outreach has been done to encourage more women into STEM, non-grade preferences remain – not necessarily good or bad, just different. I’m not sure what, if anything, can be done to equalize grade preferences between men and women. I’m certainly not going to ask women to lower their expectations and study less. That leaves grade norming to a B average. The model suggests that this would actually make a difference to the STEM gap, and it’s one that an institution could institute. Mind you, this is grade norming across all areas, i.e., STEM classes would have their grade norms moved up, while non-STEM classes would have their grade norms moved down. I’m not sure you’d get sufficient faculty buy-in to do this in the U.S., while institutions in other countries might already do this. Interestingly, one of the counterfactuals that has little effect is having more women faculty in STEM. I’m not going to comment any further on that one.

I don’t like the idea of norming to a B average. I don’t like grade norming at all. If a student showed they understood roughly 75-80% of the material, then I think they would be deserving of a B. (My B-range is pegged from 70-84%.) If the average student shows less (as determined by exams, homework, quizzes, etc), then the average student shouldn’t be earning a B. Then again, I’m the one writing the exams and setting the level of difficulty. If I made my exams “easier”, the average would go up. The question is: What is “average”?  In a Chronicle of Higher Education article twenty years ago, refreshing for its candor, a Dartmouth professor writes that “we imagine our students to be at a mythical Average U., and give the grades that they would get there.”

Maybe that’s what I’m subconsciously doing. I think C is average, and that the average student in my average class is slightly above that average (i.e., C+/B- borderline). When I have a stronger, smaller, more motivated, class – the average goes up. Not because of bias, I don’t think. I’ve tested this unsystematically by occasionally recycling final exam questions.

And now that this post is four pages long and I’m starting to wade into the phenomena of grade inflation, I think I should hold my flood of thoughts for the moment. You can wait eagerly (or not) for my next post!

First Life

What was the first living organism on Planet Earth?

Hmm… that depends. Cue the queue of questions. What do you mean by living? Why would you assume there must be a singular first? What’s your definition of organism? Isn’t Planet Earth itself an organism?

Perhaps you’ve heard of LUCA, the Last Common Universal Ancestor. Using phylogenetics, scientists have attempted a top-down approach to elucidate the bare minimum crucial functionality of the organism at the root of the Tree of Life. Yes, I called it an organism – because the entity in question shows some degree of modularity in its function with separate parts playing separate roles. Where was LUCA’s Garden of Eden? The most popular suggestions right now are hydrothermal vents, for a variety of reasons. The acidic vents seem hellish; the milder alkaline vents might not be so bad.

There might not have been one LUCA with a specific genetic code. Instead, a community of similar-functionality organisms may have co-evolved thanks to horizontal gene transfer, maintaining adaptability with variability. But here I’ve assumed that an individual is an enclosed cell-like entity; I’ve defined a (membranous) boundary to separate one individual from another. Maybe the original question should be: What was the first ecological community on Planet Earth? After all, can an individual organism be alive independent of its environment? How would it exchange energy and molecular matter to maintain itself away from equilibrium death? What is living anyway?

The thorny issue is related to how we define life versus non-life. There is no universally agreed-upon definition, perhaps for good reason. Maybe the binary boundary is not so clear cut. Multicellular eukaryotes such as ourselves might be considered confederacies of multitudes of organisms. Or perhaps we are just one big super-organism, Gaia, and those boundaries are artificial. I’m not so sure about that last one, that is, I think the boundaries are interesting in theory and certainly seem useful in practice. But where do they come from and why do we have them?

In the physical realm (acknowledging my bias as a chemist), one might first think of atoms as fundamental entities. Atom A is an individual entity. Atom B is a separate individual entity. Atom A has its own boundary, often assumed spherical, as does Atom B. But how did A and B gain their own separate identities? An introductory chemistry class might discuss protons, neutrons and electrons. Atoms are differentiated as different elements based on the number of protons, and their chemistries might be different based on the number of outer electrons.

But where did these sub-atomic particles come from? How did they differentiate from each other? A physicist might say that in the beginning was the high-energy field, and as the universe expanded and cooled, different particles condensed into existence (a sort of phase change) because of certain symmetries. There could have been a zoo’s worth of different particles, but planet Earth uses only a few types including the proton, neutron and electron. These three condensed further into mostly hydrogen, some helium, and a little lithium; and then built up some of the higher elements up to a limit. Go past 83 protons and atomic nuclei are inherently unstable, prone to radioactive decay.

Living things pick out only a few elements to use over and over again: CHNOPS (Why?) and trace amounts of metals. But these few elements lead to a huge diversity of molecules, thousands upon ten thousands. But yet again, living things condense this list into a small number of metabolites and building blocks; and this leads to a diversity of organisms of all shapes and sizes.

There’s a pattern here. It’s like a hotspot gated by a bouncer, there’s a large crowd but only a small subset gets to join the party. Open. Close. Open. Close. With double-sided funnels to facilitate the motion. You might even picture a bow-tie architecture. And perhaps this is how we classify hierarchies of individual entities. The proton. The atom. The molecule. The cell. The organism. The ecosystem. Each has its own boundary conditions. Fuzzy they may be, but this allows us at a practical level to make distinctions between entities. What is life? Perhaps this strange pattern of unity and diversity gives us some clue.

3rd Rock

Why is our planet named Earth? It privileges the most solid-like elemental phase of matter. The solid ground is where most of us live, even though Water covers the majority of our planetary surface. Air seems ethereal and transient; we might not see it but our bodies would notice its absence in our dying gasps for breath.

Earth has the connotation of soil – fertile in giving rise to flora. But increasingly short in supply. As the world’s population congregates in cities with pavements, roads, and high-rise buildings, our experience of Earth is less and less earthlike.

According to Wikipedia, Earth comes from the Anglo-Saxon erda referring to the soil. And perhaps that is how our planet got its name. Interestingly, the other planets in our solar system are named after gods, Greek and Roman, but this is a reflection of particular histories of language and culture. Other nations speaking other languages had different names carry connotations of deity and life. Gaia, the Greek goddess, has inspired the scientific hypothesis that our planet is like a single organism.

Carl Sagan called our planet the Pale Blue Dot, inspired by extraterrestrial photos from the Voyager spacecraft. TV aliens might call it “3rd Rock from the Sun”, it’s rocky-ness distinguishing it from the larger gas giants in our solar system. Today we search the astronomical skies for Earth-like cousins. There are many. We don’t know if they are pale blue, but we analyze lower frequency waves looking for bio-signatures, clues that we might not be the only living beings in the vastness of space. Oxygen, Methane, Nitrous; perhaps in a distribution out of equilibrium indicating an atmosphere coupled to a biosphere. But what we see now was light-years ago. What was once alive might now be dead. Or in equilibrium.

Persistence is a funny thing. Could it be exhibited differently in the living and the non-living? We think of rocks as persistent things, but not alive. In The Origin and Nature of Life on Earth, Eric Smith and Harold Morowitz make the interesting suggestion: “In abiotic geospheres, durable patterns are maintained because they are realized in durable entities. The dynamic order of life shows the opposite pattern: durable patterns are realized on transient entities – the core metabolites. Yet this dynamical order is arguably the oldest fossil on earth.”

The same small subset of molecules shows up again and again, appearing and disappearing in cycles of chemical reactions. It’s a different kind of persistence that makes this 3rd rock more than just a pale blue dot.