Data Detective

I’ve followed Tim Harford’s blog for many years. He’s an engaging and interesting writer, and exudes a curiosity about the world around him. I was expecting to enjoy his latest book The Data Detective, and I was not disappointed even though I’d read many of the anecdotes he shares. The book is subtitled: “Ten Easy Rules to Make Sense of Statistics”. You might think that hard data and statistics are in opposition to anecdotal stories, but the blend in Harford’s writing illustrates why both are important.

 

The rules are really rules-of-thumb. That’s important, because things are often more complicated and more interesting – something that an iron-clad hard-and-fast rule cannot capture. I could wax unpoetically about this in theory, but instead I will pick out four rules that caught my eye along with tidbits of personal commentary. For Harford’s excellent writing, I recommend reading his book in full.

 

Rule One. Search Your Feelings. The chapter begins with a quote from Darth Vader: “Search your feelings, you know it to be true.” The opening anecdote is about an art dealer, a forger, and a top Nazi commandant. I won’t spoil the story. The key point that Harford makes in this chapter is that stood out to me: “Experts are not immune to motivated reasoning. Under some circumstances their expertise can even become a disadvantage.” All this made me think about times I had gotten hot-around-the-collar about some topic. This doesn’t happen often, making these moments all the more memorable. In a bad way. Because it made me harden my position while spewing out information to support my digging in, and less open to listening. It clouded my judgment. In hindsight, I’ve been wrong some of the time. And even when I wasn’t wrong, I was less right than I thought.

 

Rule Three. Avoid Premature Innumeration. The opening quote is from Deep Thought via Hitchhiker’s Guide to the Galaxy: “Once you do know what the question actually is, you’ll know what the answer means.” I was reminded of the pitches and arguments I’ve made throughout my career as a faculty member and administrator. They’ve often “data-driven” because I try to take full advantage of the scientific veneer, justified or not. More often than not, I succeed in getting what I want. Sometimes I don’t, but I feel smug about my parting Cassandra-like warnings of the impending doom. Needless to say, hindsight reflection on the way I’ve marshaled statistics isn’t as flattering. I haven’t told lies or damned lies that I’m aware of, but I had blinkers. I was also reminded that my forecasting ability isn’t as good as I think it is. 

 

Rule Seven. Demand Transparency When the Computer Says No. I won’t tell you the opening quote except that it comes from HAL9000 from 2001: A Space Odyssey. You can guess what this is about. I’m a computational chemist who thinks about problems in complex systems; I submitted a grant on machine-learning approaches a few months ago (hope it gets funded!); I’ve been reading about algorithms and their limitations. The anecdotes in this chapter are particularly interesting – from the errors in determining “normal” body temperature to a story about a risk-assessment algorithm aimed at predicting re-arrest rates of offenders. I’m suspicious whenever anyone tells me the inner workings of their analysis are a little complicated to explain. Try me! But then I do the same thing when I’m presenting, sometimes because I’m trying not to get sucked down into a rabbit black hole, sometimes because I’m sensitive to time-constraints, sometimes because I’m obfuscating on purpose, sometimes because I don’t understand things as well as I should. Now that’s complicated. I’ll quote Harford: “Trust should be discriminating: ideally we should trust the trustworthy, and distrust the incompetent or malign.” Easier said than done though.

 

Rule Ten. Keep an Open Mind. The chapter opens with a comparison between two economists who lived through the Great Depression in the early twentieth century: Irving Fisher and John Maynard Keynes. Most of us have heard of the latter, but not the former. There’s a reason for that (read Harford’s book for the details). My reflection on this story is that if you’re successful early on, and have “often been right”, you’re ripe to colossal close-minded failure. A few setbacks along the way provide a dose of reality that can hopefully make one more circumspect. It was useful to look back at my career, recognize some of the main ups and downs, and learn some open-mindedness. (There’s also an interesting story about the Millikan Oil Drop experiment and the process of determining a fundamental constant, which reinforces the point of how we humans filter data to our own liking.)

 

Harford’s epilogue is titled: “Be Curious”. He sketches an outline of how we might reduce political polarization. I mention this to pique your curiosity. But what I’ll reflect on is the distinction Harford makes between scientific curiosity and scientific literacy. I’ve been involved in two major core curriculum designs – one from scratch for a new college, and one major redesign at my home institution. There was much wrangling over how we educate college student to be scientifically literate citizens; there wasn’t as much talk about how we encourage scientifically curious citizens. I think we want both, and I think they go hand-in-hand. I also think that many of the contemporary science education discussions muddle the distinction. This is something I’d like to chew on a bit more, before possibly blogging about it at a later time.

 

All in all, I highly recommend The Data Detective!

 

For a selection of previous blogs on books about statistics, see:

·      Science Fictions

·      Naked Statistics

·      Lying With Statistics

 

Circular Logic

The word autopoiesis was coined by Francisco Varela and Humberto Maturana almost half a century ago. It essentially means “self-making”, a key characteristic associated with life. I’ve been working my way through The Systems View of Life by Fritjof Capra and Pier Luigi Luisi. Autopoiesis is introduced in Chapter 7; the authors summarize it like this: “… the cell’s main function is to maintain its own individuality despite the myriad of chemical transformations taking place in it.” While the basic unit is the cell, this concept can be extended to multicellular organisms as a hierarchy of autopoietic units.

 

Earlier in their book, Capra and Luisi tackle the problem of reductionism – the idea that the whole can be reduced into its parts, and nothing but its parts. Emergentism, in contrast, argues that the whole cannot be reduced as such. One way to acknowledge the usefulness of the former without dissolving the latter is to distinguish structure versus property (sometimes called function). While most chemists utilize the rule-of-thumb that structure leads to function, in reality this causal relationship is not so straightforward. Capra and Luisi aver that reductionism is kosher (and useful) when “it limits itself to structure”. On the other hand, “emergent properties cannot be reduced to the properties of the parts”. It then follows that “life as a property cannot be reduced to the properties of the single chemical components.”

 

The authors provide a diagram to illustrate the “cyclical logic of cellular life”. Above is a quick blurry snapshot I took in less than optimal conditions by holding the book up to my laptop camera. One might claim that such circular or cyclical systems are fundamental to the logic of biology – the “bio-logic”. No need to squint at the figure caption as I’ll provide it momentarily. Note that the larger whole includes both the cell (the “bounded system”) and the environment (yet another system?). The circle indicates that the bounded system is “operationally closed”. Note that energy and materials can still flow into and out of the bounded system. This is unlike closed equilibrium thermodynamics systems students learn about in chemistry class, where energy can flow between the system and environment, but not materials.

 

I’ve reproduced the diagram below using PowerPoint and its accompanying caption.

 

Figure 7.3. The cyclical logic of cellular life. The cell, an autopoietic unit, is an organized, bounded system that determines a network of reactions that produces molecular components that are assembled into the organized system that determines the reaction network that… and so on. The terms “input” and “output” – inobservance of the fact that the ell is an open system – represent respectively the incoming of nutrients and energy from the outside, and the outgoing of waste products. The circularity illustrated in the figure corresponds to the notion of operational closure, giving rise to the broader notion of biological autonomy.

 

A brief tangent: Did I reproduce the diagram and text or did I replicate it? I’d say reproduce if this was a physical copy because I didn’t use the same ink or the same tools. Even digitally, the 1’s and 0’s that make up my picture are quite different from the digital photo I took. But if the book and figure were themselves digital and I made a copy of it (with the same sequence of 1’s and 0’s), would this then be a replication? Hmmm… But back to living systems, I’d say DNA replicates but a cell reproduces.

 

In a living system maintaining itself, you’d expect the rate of input to be equal to the rate of output. Students should recognize that this indicates some sort of balance – we call this steady state. Don’t confuse this with the equilibrium state where the rates of all forward reactions and their corresponding reverse reactions are equal, and the free energy of the system is at a minimum. At steady state, energy and materials continue to flow through the system and the cycle likely has a non-zero change in free energy.

 

If the rate of input is larger than the rate of output, than growth can take place. Conversely, if the rate of output is larger than the rate of input, the organism is shrinking, possibly losing both materials and energy – perhaps death is taking place. That’s one way to think about life and death as a continuum of activity. Some organisms even have a period of stasis where all the rates slow down, a state between life and death that we might call suspended animation.

 

Concluding their chapter, Capra and Luisi show a Venn Diagram with three overlapping circles labeled environment, cognition, autopoietic unit. They call this the trilogy of life and claim that “it does not make sense to consider each of these three domains as independent of one another”. Interestingly, I have a similar Venn Diagram with the labels information, interaction, identity – which might substantially overlap with the respective domains described by the authors. Why do Venn Diagrams typically use overlapping circles, but not other shapes? I don’t know. But there are a lot of circles and cycles we see used to describe the logic of life.

A Hack Too Far

This is how they tell me the world ends.

 

That’s the title of Nicole Perlroth’s explosive book detailing her beat as a New York Times journalist with the thankless task beating the bushes for nuggets of cyberwarfare and cybersecurity. Most hackers avoid her. Some speak to her, but obfuscate in their answers. But a very few give up gold – enough to put together an intriguing and scary tale that will have you questioning your phone, your computer, your Nest products, and anything you do on the Internet.

 

I now see news articles related to tech in a different light – yesterday’s Kaseya ransomware attack, or today’s announcement of the Didi app under scrutiny. Hacking is big business globally and growing at an exponential rate thanks to the cyber arms race as detailed by Perlroth and others. I’ve seen how easy it is for an individual with not much experience to get off-the-shelf hacking tools. Governments and nation-states have orders of magnitude more resources at their disposal, and they use it. And there’s everything in between. Perlroth begins the story with zero-day exploits, but things get heated up with the sprouting of cybersecurity start-up outfits, both for defense and attack. The money that flows through has also increased exponentially. What Snowden reveals is only the tip of an iceberg.

 

Coincidentally, I’ve been watching the TV series Person of Interest. God’s-eye A.I. is a prevalent theme in entertainment this century. While you might classify this as “action-drama”, I’m starting to think that “horror” might be an apt inclusion to the genre. There are some cool action sequences, but the interesting part of the story is the evolution of the A.I. and what people are trying to do with or to it. Some want to protect it, some want to use it, some want to break it, and I’m sure more motives will be uncovered as new characters are added to the show. (I’m in Season 2 out of 5. The dialogue is sometimes crummy, but overall the show is engaging enough for me to keep watching.)

 

There’s what we know, and there’s much that we don’t know. Perlroth gives the reader an engaging peek into the underbelly of cyber breaking-and-entering. Yet she regularly admits that she has bits and pieces, strung together by what sources are willing to tell her, or inadvertently let drop. And they’re a tight-lipped bunch. The whole point of a zero-day, before it becomes known, is to embed itself as long as it can in secret while stealing your data and possibly doing extra damage. If you’ve heard of Stuxnet, you know what I’m talking about. Yet there is likely much more, and most of us are blissfully unaware.

 

Everything is plugged in now. For automation, for efficiency, for sheer complexity, and for many other reasons. The energy grid. The food supply chain. The monetary system. Traffic lights. Air traffic control. GPS. We’ve seen how a tiny coronavirus can multiply and quickly infect humans in a globally-connected system. I suspect it’s only a matter of time before a cyber virus does something similar. Likely the only reason we know about Stuxnet is because it ‘escaped’ and spread far beyond its confines, and we’re lucky it didn’t do more damage because it was ultra-specific in what it was looking for. We might not be so lucky next time.

 

It wasn’t that long ago when I taught classes without the internet. I wrote out my own lecture notes. Made photocopies of problem sets and handouts. In class I mostly used the board interspersed with occasional overhead projector slides. I think my students would adapt, and possibly even concentrate more in class without their digitally-connected distractions. As a computational chemist, my research program in its present form would die without the internet. Adaptation would require very significant change in how I do research and the questions I would seek to answer.

 

But my work-life is only a slice of life in the modern era. The chaos of a cyber-virus gone wild is, I think, a realistic threat. A hack too far. And the boundaries are being pushed. This may be how the world of modern humanity ends.

Year Streak

Today, I completed my 365^th day in a row with Duo the owl. I haven’t had to use the streak freeze purchased in those early days; it’s still equipped. I suppose being stuck at home during the pandemic and not traveling anywhere helped the streak.

 

Lately Duo has started sending my weekly statistics. It looks like I spend just shy of two hours per week. That translates to approximately a hundred hours for the year. If this was equivalent to a three-credit-hour class, I might have completed the equivalent of first-quarter Spanish, or two-thirds of first-semester Spanish – but it took me a whole year rather than ten or fifteen weeks. All this is to say that I’m not particularly proficient.

 

My reading isn’t bad, and I can read a sign or a notice in Spanish should I encounter one somewhere around town. These are almost always accompanied by English translations. But my listening comprehension is poor. If it’s a short single sentence and I know the vocabulary, no problem. But if it’s a longer sentence, or multiple sentences are spoken, I have trouble because while I’m still translating the first part in my head and my brain shuts off listening to the rest.

 

After my first 150 days with Duo, I attempted to listen to the first episode of the Duolingo podcast. I failed miserably, because even though the narrative was at a leisurely pace, there were typically several sentences in a stretch. Now with 365 days under my belt, I tried again. Better this time around, but still not great, when my eyes are closed. I found myself regularly opening my eyes to read the text, which allowed me to follow along fine. My reading comprehension is indeed better than my listening.

 

I’m sure my chemistry students have the same experience. In introductory chemistry (especially for non-majors), it must seem like I’m speaking in a different language. Now I’m trying not to be jargon-y and I’d like to think I’m watchful about my pace, for all I know it could seem like Uncleftish Beholding to some students. Especially if they’ve never had a chemistry class in high school. Most of my students have heard the basic terms so it’s usually not too bad for them, until we get into organic chemistry and naming functional groups.

 

In physical chemistry, the problem is math. While all the students have taken the pre-requisite math courses, at least half of them are not fluent enough in it. Decoding is slow when they see an equation, or worse, a derivation. That’s like me trying to decode the first part of a sentence while the speaker has moved on. I always write on the board because it helps slow things down and also allows the students to follow more closely step-by-step what’s going on, but it’s still a challenge. Not that my math is particularly strong. I experience the same novice-ness when reading math-dense papers for research. All this is to say that it’s good to be reminded what it’s like to struggle learning something new. We teachers should experience it regularly to be reminded how it feels to be a novice.

 

Unlike mid-year where my motivation for Duolingo plummeted, I’ve been catching my second wind. I’m now doing around 70 XP per day, a slight uptick, but not as high as when I first started and was much more motivated (doing 100 XP per day). Here are my current stats. I have 2397 lingots and there’s nothing much to spend them on. I have no plans to upgrade to Duolingo Plus. I have 369 crowns, i.e., I’ve averaged more than a crown per day, so not too bad overall.

 

No reason to stop now. I’m not sure how far I’ll go, or whether my motivation will plummet again. I’m glad that Spanish has Stories. And I might try a few more of their Podcasts. We’ll see if I get to the two-year mark!

Shades of Gray

“What is Life?” is a difficult question to answer, perhaps because there is no clear boundary between life and non-life.

 

An alternative approach is to posit a continuum between life and non-life – shades of gray between the clear black and white cases. A month ago, a raft of sixteen co-authors published a perspective article: “The Grayness of the Origin of Life” (Smith et. al., Life 2021, 11, 498). They see this continuum in five areas of chemistry: organic molecules (biotic and abiotic), information storage systems (nucleic acids and derivatives), metal catalysts, energy-transduction molecules, and compartment-building molecules. In each case, there’s a continuum composed of different shades.

 

Their conclusion: “A grayness persists throughout biology. This is particularly true in life’s earliest transition from geochemistry to biochemistry. This grayness is associated with the degree of chemical disequilibrium. Life exists in disequilibrium from the environment and maintains this disequilibrium, using it to perform work. While the equilibrium state might be clearly abiotic, there is no inherent threshold to cross into the biological. Of these gray intermediate states, the distinction between proto-life at disequilibrium and a metastable mineral, for instance, is hard to discern.”

 

The practical outworking of this might be to come up with a question list. Does it reproduce? Is it self-sustaining? Does complexity increase? Is evolutionary change and adaptation possible? Is energy transduced? Are there cells or compartments? Clear-cut cases would check all the boxes or none of them. Shades of gray would be checking some of these boxes. Life-like might be what we call these systems or organisms. Are some of these check-boxes more important than others? I suppose there’s the rub. We might not be able to come to agreement on these, and perhaps that’s as it should be – if indeed the complex is not reducible to parts.

 

At the end of his book, Life’s Edge, Carl Zimmer posits an analogous question to make the point that such ambiguous questions are common. “What is a game?” As someone whose hobby once-upon-a-time was playing board-games and card-games, I found this an interesting question to ponder. Presumably a board-game is a game that has a board; a card-game is a game that uses cards. So those were easy to define. But what is a game? (You might also ask: what is a board? what is a card?) That’s not so easy to define. Not all games have boards or cards or counters or dice, and some may not require any “equipment”. But they do all have rules or some sort of constraint. These rules are idiosyncratic to each game. Presumably they have players that engage the game, either solo or with a crowd.

 

To play a game, you learn the rules, and if the game is interesting and engaging, there will be multiple potential outcomes and layered strategies. You won’t appreciate these without multiple plays. Is our present “game” of life one of the more engaging ones? I think so, or I wouldn’t be studying it. Are we pawns in this game? I don’t know. Can some of the rules be bent or broken? I suppose it depends on the open-endedness of the system. That’s what makes it complicated. And perhaps shades of gray is the appropriate approach to such questions. Or Venn Diagrams.

Cryptobiosis

“A physiological state in which metabolic activity is reduced to an undetectable level without disappearing altogether.” – the definition of cryptobiosis from Oxford Languages.

 

Several weeks ago, Current Biology published a paper on the revival of 24,000-year-old microscopic animals called rotifers from the Siberian permafrost. This seeming resurrection from the dead was first observed by the famed microsopist Van Leeuwenhoek, way back in 1701. His sample of rotifers came from reddish gutter water from his house. Intriguingly, the organisms shrank in size as they dried out. Gutter water turned to dust in the dry and hot summer. Water was added and behold – the rotifers came back to life!

 

The rotifer vignette is one of several stories of organisms perched near the fuzzy boundary between life and death in Carl Zimmer’s new book, appropriately named Life’s Edge. Forty years after Van Leeuwenhoek’s discovery, nematode worms were found by famed naturalist Needham to survive several years in dormancy. Then came tardigrades, sometimes known as water bears, well-experimented on by modern day scientists and known to survive the vacuum of outer space, temperatures close to zero kelvin, and being hit by speeding bullets.

 

Water resurrects these creatures in the twilight zone.

 

How do they do it? According to Zimmer, some species produce a sugar, which “thanks to its chemical structure, trehalose can help proteins keep their proper shape, much like water does.” Others “make a new batch of proteins that link together to form a kind of biological glass. It entombs the cell’s DNA and other molecules in their three-dimensional form, so that they’re ready to revive when water returns.”

 

While larger creatures have not been classified as cryptobiots – we can detect their unusually low metabolic levels – many of those in colder climates use a hibernation strategy when winter arrives. Bears do it. Bats do it. Bees do it. Even one species of bird. (Most others migrate.) Trees do it too. Could humans do it? Maybe. If we tipped into another ice age. And if you believe the situation in Early Riser is possible.

 

In his short Origins of Life book, Freeman Dyson proposes a simple toy model to model the transition between the living and the dead. The two states are ‘attractors’ in complexity theory parlance, and the hilltop between the two is an unstable transition state. With simple assumptions and parameters, Dyson calculates that a population of 2,000-20,000 containing 6-8 monomeric species (that also serve as catalysts), with a discriminative factor of 60-100 (modern enzymes are a hundred-fold more discriminatory) allows for moving from being dead to alive.

 

The caveat? In Dyson’s words: “The basic reason for the success of the model is its ability to tolerate high [reproductive] error rates. The model overcomes the catastrophe by abandoning exact replication. It neither needs nor achieves precise control of its molecular structure. It is this lack of precision that allows… [a] jump into an ordered state without invoking a miracle.” This also means it’s easy to die without invoking a miracle. By changing the parameters in line with evolutionary processes improving the fidelity of replication, Dyson’s toy model shows that life is more easily preserved, but resurrection becomes much, much more difficult.

 

As someone focusing on the chemical origins of metabolism, I find Dyson’s model intriguing. Coincidentally I’ve been considering numbers not too different from his (based on thermodynamic and kinetic data), at least in my small and restricted model although much bigger than Dyson’s and requiring orders-of-magnitude more computer time. The idea of treating such a chemical system as cryptobiotic is a possible framework. And water may play a key role, as we’re seeing in recent wet-dry cycle origin-of-life experiments. Maybe I’m into the business of resurrection after all – or perhaps just resuscitation.

The Quick Fix

Life hacks. Self-improvement. Quintessential American traits. Makes for good clickbait: Transform your life through this one simple trick!

 

Also, good fodder for TED talks. In the early days, some of these might have featured substance over showmanship. I no longer watch them. Hyped-up chaff has become dominant; thin on substance and thick on sales.

 

Jesse Singal takes on several of these hyped topics in his new book The Quick Fix, appropriately subtitled “Why Fad Psychology Can’t Cure Our Social Ills”. His book is complementary to Stuart Ritchie’s Science Fictions (here’s my post on it) which focuses more on the statistics. While Singal also points out the statistical problems, he focuses instead on why we’re easily drawn into such fads and the history behind their meteoric rise. Topics include the self-esteem movement, super-predators, and power posing, among others. The one that’s most related to education, and we’re still in its grip, is Grit – the topic of today’s blog post.

 

I was excited about grit when it burst onto the stage, championed by Angela Duckworth, who has an interesting backstory. I viewed her TED talk, and even before I read her book, I had started to read the primary literature and felt positively disposed towards the idea. But over the years as I’ve followed the primary literature, I’m much less sanguine the effectiveness about so-called grit interventions, and even its usefulness as a construct. For example, Conscientiousness (from the OCEAN big-five personality traits) seems to correlate better to some measures of student “success”, but there’s still much to quibble about even from the larger meta-studies.

 

Singal opens his chapter on grit with the following: “Grit is everywhere. By the time you read this, it will have been a golden child of the world of education for well over a decade. It’s a sexy, appealing idea: grit predicts success, grit can be measured, and grit can be improved.” Given my own prior reading of the primary literature, I didn’t learn much that was new from Singal’s take, although I did appreciate his historical narrative and the personalities involved – something one doesn’t quite get from reading the primary literature.

 

Grit is appealing to me as an instructor. I teach chemistry. It’s hard. Students both complain and acknowledge this. Don’t expect to breeze through the class. You have to persevere and put in the hard work. Even if you do, you might not be successful. But if you don’t, you’re unlikely to do well unless you’re some sort of super-genius. Now I’ve had students breeze through first-semester G-Chem if they’ve had a strong AP Chem or Honors Chem class in high school. But that’s because much of the material is a repeat for them. Second-semester G-Chem is a little harder, but if the students have seen the material before, they still do well – but they actually have to put in some work. Not a single student has told me that P-Chem was easy in my twenty years of teaching it. I suspect every single one of those who got A’s (and there aren’t many) will say they spent many hours studying and working on the problem sets. So would those who got B’s and C’s, for that matter.

 

The problem with Duckworth’s book (Grit: The Power of Passion and Perseverance), according to Singal, is that the vignettes are happy stories of the winners: “… we don’t really hear anything about hardworking, gritty, resilient people who don’t get as far as they would like to, or who fail spectacularly; the losers are nowhere to be seen.” It’s what I see in P-Chem. Perseverance may be necessary. But it isn’t sufficient. Whether or not one is ultimately seen as “successful” depends on a whole range of other factors, some of which are structural, some of which are not under one’s control (be it student or instructor). Grit can get you out of the hole in some circumstances, but not others.

 

A common thread on the psychology fads that Singal writes about, is that there is an element of truth in all of them. That’s why they resonate. That’s why one can make up a plausible explanation of why they work, at least in some cases (usually in simplified “laboratory” conditions removed from the real world). The most clickbait-y ones are low-hanging fruit. Simple life hacks. But they over-promise. A small life hack will likely not lead to a large transformation, at least in most cases. Yes, there will be some outliers, and the positive outliers make great stories. For the average person, a simple optimization could lead to a small improvement. And after you’ve done the small optimizations, you hit a dead end. Unless you’re willing to “disrupt” and make large changes. And there’s always someone willing to sell you something to make that transition easier – technology in education is one I’ve been thinking about lately.

 

The truth is that many things are outside our control. Larger structures and systems are not easy to change. When you run up against them, you feel stuck. Like you’re banging your head against the wall. All your passion and perseverance may lead to nought, not to mention your ingenuity, creativity, and whatever other positive trait is celebrated as being the savior. The quick fix doesn’t get you very far. Worse, if you are one of the haves in society who likes the idea of grit, you might see the have-nots as lazy and not persevering enough. That’s the dark side of meritocracy.

 

Singal closes his book discussing priming and nudging – also popular fads in education today. If you’re interested in these, and more, I recommend his very readable book, with an extensive index for those who want to delve into the primary literature. His take on finishing the book in the midst of a pandemic are interesting, and I’ll quote him in closing.

 

When the virus arrived in the United States, Americans’ choices were, as always, defined by big, complicated structures of power and wealth. Some Americans in the pandemic’s epicenters were forced to choose between financial ruin and continuing to work low-wage jobs in which they faced infection, while others were able to make a fairly seamless shift to working remotely. It would be impossible to overstate the significance of these divergences: no, having money didn’t render anyone immune from the virus, but overall one’s chance of riding this pandemic out safely and in relative comfort had everything to do with the resources at one’s disposal, which, as usual, meant that shocking racial disparities soon emerged. Structural forces went a long way toward dictating who lived, who died, who struggled, and who was relatively unaffected. They always do.