The Professor and the Madman: Review

I heard myself mention to a friend one day, “I’m reading this great book about the making of the Oxford English Dictionary.” This comment was followed by a pause as I thought to myself, that feels like a weird thing to have just said, and as she (probably) thought to herself, this girl is getting geekier by the day.

The book truly is about how the OED came to be, but reads more like a novel. Simon Winchester gives his readers an appreciation for the magnum opus that is the dictionary. In a world without the Internet or other good dictionaries to use as precedents, the people working on this project had to read extensively, documenting and defining every new word they came about. The OED goes beyond this, though, because it includes examples of the word in context – examples that really make its meaning clear. And the dictionary makers were careful to include examples from different time periods, in order to show the changes in usage that a single word has undergone during its life. All of this had to be coordinated among a changing team of numerous contributors distributed across many locations (did I mention yet that there was no Internet? This feat alone blows my mind).

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In addition to imparting an appreciation for the complexity of the project, Simon Winchester shares much about two of the most influential men involved (the professor [James Murray] and the madman [William Minor]). Readers get a sense of these mens’ lives – for example, that William Minor was a doctor during the Civil War, forced to brand a deserter’s face with a hot iron – and how their pasts shaped the men they were as they worked on the project. There was hardly an antagonist (though there were characters that posed trouble at times). Instead, I was rooting for everyone all along – for Murray, Minor, and for the dictionary itself.

This book rekindled my appreciation of stories, quirky genius characters, words, and massive, seemingly intractable projects. It simultaneously inspired me, and made my own work feel like a picture book in comparison.

Metaphors of the 2016 Presidential Election

I discovered today that if you Google a candidate’s name + “issue stances,” Google provides a list of key political issues and quotes from that candidate on the issue. As someone who tries to remain an educated voter but doesn’t particularly enjoy politics, I was excited to find what essentially seems to be the Sparknotes for voting.

Political discourse is known for being full of metaphors, and the quotes Google provided were no exception. Here are some of the more colorful ones I noticed and the ways they may shape the way we think about the issues*:


Donald Trump repeatedly uses the metaphor of immigrants as water: “We cannot allow illegal immigrants to pour into our country,” and “These are people that shouldn’t be in our country. They flow in like water.” When water is flowing, and especially when it’s pouring, we infer that it’s coming fast and consistently. Further, when too much water comes too fast (like when it’s pouring rain), we end up with flooding, which can destroy infrastructure and the homes and lives that people have worked hard to build for themselves. These inferences seem to be consistent with Trump’s stance on immigration: if we don’t cut off the metaphorical faucet, we could all end up drowning, watching all that we’ve worked for float away.

Clinton and Sanders use a journey metaphor for people who are in America illegally, often referring to a “path to citizenship” (a phrase they both use) and providing a “roadmap to citizenship to the 11 million aspiring Americans living in this country” (Sanders). Paths are intentional and defined, and when we stay on them, we eventually end up at a destination. Sometimes staying on a path does take some physical effort, but each step results in progress. Just as immigrants travel to arrive in America, once they’re here they will metaphorically travel towards citizenship. The inference accompanying this metaphor is that the Democratic candidates will further define and structure the path (perhaps by clarifying the roadmap) to allow immigrants to become legal citizens.


The Democratic candidates have made some revolutionary statements about education, especially college education, that point to their goals to drastically reduce or eliminate students’ college debt. Hillary Clinton has referred to creating an “education SWAT team” of qualified people who would define education standards for the country. A SWAT team comes in to restore order during times of chaos and danger, so the SWAT team metaphor implies that our education system is in dire need of dramatic, immediate help. Bernie Sanders shares a similar viewpoint by sarcastically referring to the “crime of trying to get an education.” Just as criminals often have to pay burdensome fines for their actions, many college graduates find themselves with unrealistic amounts of debt after college. By likening college debt to criminal fines, Sanders implies that it is ridiculous that graduates and criminals both have similar punishments. If you buy into this metaphor, his plan to make public colleges free seems like a no-brainer. Donald Trump, on the other hand, makes few substantive comments on education, so his metaphors are not clear.

National Security

The metaphors for elements of national security seem to be a little more varied. On the side of avoiding too much security, Sanders says that “We can (protect the country from terrorism) without living in an Orwellian world.” You could argue that the reference to an Orwellian world is technically an allusion, but it’s also a metaphorical allusion. In George Orwell’s 1984, the government knows everything about everyone, and the book is a creepy warning for what can happen if the government oversteps its boundaries. This reference encourages people to think of the vague notion of the government increasing its intelligence efforts in more concrete terms but conjuring up the disturbing images from 1984.

There are also many metaphors that suggest that national security should be increased. Clinton’s language suggests that terrorism threats are concrete, material things, referring to “the threats we face together” and “The threat we face from terrorism is real, it is urgent, and it knows no boundaries.” People have a hard time thinking about things they can’t see or touch (or otherwise experience directly), which is when metaphor often comes in. Terrorism threats are a great example of a complex and intangible problem, but by suggesting that they are real things that we can face and that they can spread without boundaries, Clinton encourages people to think about them more concretely, which can in turn encourage us to take national security more seriously.

Trump often talks very literally about a wall around our borders. This is not a metaphor, but would be an especially vivid one if he wasn’t being serious. He does talk about our current border status metaphorically, however, claiming that “our borders are like Swiss cheese.” If only he were being literal about that border claim, we’d have a lot of happy people!

* Here are the Google searches that turned up the quotes that I use throughout this post: Trump, Clinton, Sanders.

Lab Girl: Review

I wasn’t sure what to expect when I discovered Hope Jahren’s new memoir Lab Girl because it’s listed under the genre of Environmental Science, a type of book I don’t usually gravitate to. It didn’t take me long to realize that the scientific world has been begging for this book to be written for a while. As I’ve continued to immerse myself more deeply into academia, I’ve realized that the massive rift separating the Ivory Tower from the rest of the world is not narrowing. Lab Girl is an account of one woman’s journey toward and through academic science, a glimpse of what a scientist might actually be like as a person and what it means to conduct scientific research as a career.


Jahren’s story starts when she was a little girl, spending hours in her dad’s lab, and continues to cast glances into her life as a young adult, graduate student, assistant professor, and finally as a tenured professor. Her road was anything but smooth. While many features of her path were unique, so many were not. Financial struggles were a theme throughout a good portion of the book. First, she had to pay for her education as an undergraduate, and once she completed grad school, she had to apply for competitive grants to afford her lab, her right-hand man Bill’s salary, and her own. At one point she writes about buying a bunch of fast food burgers when they were on sale and freezing them for future lunches. She also writes about periods in which mental illness overcame her daily life and left her unable to function. Again, the Ivory Tower might seem like a utopia where everyone is happy and nobly working toward the pursuit of knowledge, but such struggles are not so rare among the ultra-driven academics who have never failed a test in their lives and are now pursuing PhDs or esteemed faculty positions. And she writes about the tedium, discomfort, and anxiety involved in doing science, like meticulously labeling vials and taking long road trips to dig up and study the earth in new (often desolate-seeming) locations.

Writing about these less glamorous moments and years sends the message to other academics, you are not alone. This shit is hard. And it sends a message to non-academics that the road to becoming a successful scientist is not paved with gold. Jahren adds even more value because she’s a female scientist, and although she doesn’t belabor the point, there are many stories that shed light on the extra hurdle that many females experience in science.

Jahren paints a clear picture of what doing science was like at different stages of her life, while also shedding light on what being she as a person was like at different stages and interspersing short chapters that expose trees’ beauty and complexity. Lab Girl is a love story between Hope Jahren and science, exposing their relationship’s joys and challenges and showing the readers that all along these two were meant to be together.

Do babies matter? A review

I was very excited to find this book by Mary Ann Mason, Nicholas Wolfinger, and Marc Goulden: Do Babies Matter? Gender and Family in the Ivory Tower. The authors deal with the complex and multi-faceted relationship between families and academia in an organized and data-driven way. They use detailed survey information to present the beliefs and career decisions of academics (especially women) at different points of the academic “pipeline,” from graduate students through tenured faculty members and how these relate to two of the most typical milestones for family formation: marriage and childbearing.


As a married female graduate student who loves much about academia and also hopes to raise kids eventually, this book’s agenda is important to me. After reading the book, there are a few undeniable takeaway points:

  • Women, especially at the earlier stages of academic careers (PhD students and postdocs), are more likely than men to perceive raising a family and obtaining a tenure-track faculty position as incompatible goals.
  • Academic institutions lack flexibility that exist in other professional fields like law and medicine like alternating between full- and part-time work or taking maternity and paternity leave after a birth. Even when academic institutions do have these policies, people often do not know about them or are hesitant to use them because of their associated stigma.
  • Women (and especially mothers) are underrepresented at the top of the academic career ladder.

There are lots of injustices in the world, and academia is not immune. Whether we want to or not, humans have subconscious biases, and these biases take a ton of work to overcome. Bringing awareness to discrepancies is a crucial step toward eliminating them, and this book does a great job of doing just that. There are a few recurrent underlying assumptions, though, that didn’t sit right with me as I was reading this book.

  • Tenured faculty is the ultimate goal. For many grad students, this is true. In fact it is a waste of a graduate education if the recipient is not going to remain a competitive academic researcher. In a paragraph about how “Many of our best and brightest young people are rejecting careers at research universities,” the authors write that “The United States cannot afford to lose many of its best researchers and thinkers, scholars who will eventually train the next generation. And these talented young scholars should not have to forsake careers for which they have already invested many years of their lives.” If PhDs take jobs outside academia, the United States is not losing them at all. Their training isn’t going to waste, it’s just going to a different use than many people assume it is “supposed” to go to. Not to mention, many people don’t look at getting a PhD to be career training in the sense that getting a Nursing Degree or even a Master’s Degree is. You do a PhD to gain experience, thinking, communicating, innovating, and answering nearly intractable questions. Academics love to say that you don’t get a PhD to get rich (though a job is pretty universally expected at the end).
  • Correlation and causation… There are times when the authors do remind us that statistics don’t allow us to make causal claims, but other times when the authors seem to forget that crucial notion. Comments like “Marriage also leads women to leave the labor force. Compared with an unwed woman, her married counterpart is 28 percent more likely to not work.” It may be true that marriage is the reason these women leave the labor force. Or perhaps women who leave the labor force have more time for dating and get married at higher rates (that’s fairly ridiculous, but technically possible based on the statistic). Or perhaps there’s some underlying personality difference between women who choose to get married and to stop working and those who don’t, a hidden variable responsible for the different work behaviors that isn’t marriage at all, but instead tracks with marriage. What if marriage is so fulfilling and stabilizing that women decide they don’t need to keep working at jobs they’ve hated?
  • Women and men have the same career goals and desires. This follows from the assumption above. Men and women are biologically different. It’s a good thing, too, because that keeps humans on the earth. These biological differences are pronounced in parenting. I don’t doubt that dads and moms can love their kids equally, but women carry the fetus for 9 months, give birth, and often feed the baby milk from their own body. As they’re raising a human being (or multiple humans, as is often the case), women may decide that their former jobs don’t provide the same meaning that parenting does. They may cut back on work or cut it out entirely, and this might be a great thing for many women. It is a luxury to be able to make this choice. And in some families, it may be the father who makes the choice and the mother who continues to work, but I don’t think that biology has set us up for that to be the majority choice. The statistics about women who remain in R1 (top research) faculty positions and those who take less demanding roles or stop working altogether are presented as proof enough that women are underachieving because of families. If it is a genuine choice that a woman makes to prioritize family over work, isn’t that quite an achievement?

Crucially, it needs to be possible for women to be successful researchers, wives, and mothers if that’s what they want. I believe that is the authors’ motivation, and they give suggestions for ensuring this possibility. But women who leave the pipeline shouldn’t be considered failures, and their decision should not necessarily be chalked up to injustice. It’s a really messy issue, but it won’t get better unless we keep talking about it as this book has successfully prompted many to do.

A glimpse of high school science education through the lens of a science fair

Last week, I volunteered to judge the Greater San Diego Science and Engineering Fair. I found the experience interesting last year, so I participated again this year. Judges have about 3 hours to visit 12 projects and discuss the contents of the posterboard and research journal with the student who did the work. These conversations allow the students to tell us what they did, but they also give them practice at answering questions they may not have anticipated, and the conversations give judges the opportunity to gently teach and discuss things that could have made the project better. Last year, I judged middle school behavioral science projects – unique questions that 6th, 7th, and 8th graders came up with about human behavior, and often equally unique ways of testing those questions. The students put in a lot of effort and did great work, but judging the projects also made me realize that science is really hard. They still had so much progress to make before these projects could even be considered sound, let alone innovative or informative.

But these kids were on their way! I felt that if I could see their science fair projects a few years down the line, my mind would be blown by their progress. This year, I was assigned to the high school division – exactly the opportunity to see the progression of scientific thinking. I knew these students had been working on the projects since the beginning of the year, and many had dedicated class time for guidance. But after visiting just a few projects, I was let down. I wasn’t let down by the students – they were all so earnest, pleasant, and proud, and it was clear that they had put a lot of work into their projects. Instead, I was disappointed with their teachers. So much of the scientific process needs to be explicitly taught, and for some reason, these kids weren’t taught it. Either the students weren’t getting the guidance they needed, or they were actually being misguided.

Imagine that someone eats eggs for breakfast on every weekend day, and never on a weekday. That person tells you, “every time I eat eggs, I have a great day. I guess having eggs for breakfast must cause my day to be good.” You’d probably quickly object to this conclusion – what if the fact that it’s a weekend causes your day to be good? In fact, this explanation seems likely. One variable in your “experiment” is whether the day is a weekend or weekday. The other variable is whether you ate eggs, which happens to vary with the weekend vs. weekday variable. In this case, if you want to know the effect of eggs on the quality of your day, the type of day (weekend vs. weekday) is a confound. It makes it impossible to attribute the results you saw to the variable you want to attribute it to. I saw lots of these egg/weekend confounds in the students’ problems, which is alarming because they invalidate the results the students tried to convey.

For example, one student tested the effect of different font colors on reading speed. She pulled three equal-length passages from a book. One passage she left in black font, another she turned orange, and the third she made multicolored. She then had all her participants read the three passages while she timed them. She found that they were fastest to read the passage written in black ink. But wait – what if that passage just happened to be an easier passage to read? Wouldn’t that account for her results, without taking ink color into account? She thought about this, and then agreed. Together, we worked through the solution that would have avoided the confound – if some people had read passage A in black ink, others had read the same passage in orange, and still others had read that same passage in multicolor, and then we did the same with passages B and C. This way, everyone would have read each passage once and experienced each ink color once, but that the passage-ink pairings would have differed for everyone. This is counterbalancing. Counterbalancing is done specifically to avoid confounds.

These kids of errors were evident in many projects. Another pair of students presented people with songs once to see how much of the chorus they could remember. Oddly enough, they used two songs with the same lyrics in the chorus, but extremely different melodies. They presented Song A first for everyone, and had them recall the lyrics. Then they presented Song B, and had them do the same. Perhaps not surprisingly, people recalled more lyrics for Song B. The students told me this was because Song B was a more familiar genre to their participants. While that’s a possible explanation, it’s not a scientifically valid one. Their participants all had more practice by the time they got to Song B, which had the same chorus as Song A. They should have been better at the latter simply because practice improves performance.

Luckily, as I gently explained these confounds to the students, something seemed to click – they could see the logical problems in their methods and conclusions. A few mentioned to me that they didn’t counterbalance important variables because their teachers told them to keep as much constant as possible. Normally, this is true – you want to keep as much constant as possible when testing different conditions so that variations don’t make your results noisy. Noise in data makes it harder to detect real effects. But the teachers forgot to impart an important caveat of the keep-everything-constant rule: You can’t keep things constant when the constancy could explain your results – when it could become a confound!

This opens an important question for me – were the teachers not able to give guidance on these fundamental logical ideas for doing science? I realize that they have many students to oversee. Or do the teachers lack an understanding of how experiments should be designed, implemented, and interpreted? My intuition is that it might be some of both, but it seems to be pretty problematic, regardless of the source. Allowing students to carry out months-long projects that violate important rules of scientific logic seems like a very bad way for them to learn how things should be done.

But then I started to wonder, do these students actually need to understand how experiments are conducted? Do they have to know why confounds are to be avoided at all costs and how to do so? Many will pursue non-scientific fields. Others will pursue science to the extent that they might not ever need to conduct research, and will get by learning the things that other scientists have found and trusting those scientists’ conclusions. And the students that do pursue scientific research can learn from their future mentors how to conduct science (and how not to). Maybe this is all true, and maybe I can chill, but I’m still thinking about these questions almost a week after the fair, so there must be something to my concern. Shouldn’t educated citizens be able to understand the scientific process, so they can understand why scientists make claims about global warning or about how innocuous (and important!) vaccines are? I’m not sure, but these are some of the questions I’m trying to work out.

Curious: The Desire to Know and Why Your Future Depends on it (Review)

One day as I was clicking through Amazon, the site recommended a book with the word Curious across a black cover with an owl beneath. Naturally, I was curious: A whole book on curiosity? How much is there to say? About 45 seconds later, I was reading it. It was a fun read, peppered with stories, descriptions of research, and historical anecdotes. It was filled with rich quotes, by the author and many others that have written about the topic over centuries, and I’ll let those quotes drive this review.


A Taxonomy of Curiosity

Curiosity is not just one thing. Ian Leslie describes three types of curiosity, distinguished by the contexts in which they arise and the behaviors they encourage us to seek out.

Diversive curiosity is an attraction to things that are novel. I imagine a dog on a walk, pausing to inspect every seemingly new patch of dirt, trash, or fire hydrant. Humans show a lot of diversive curiosity too, like when we scroll through a Twitter feed or flip the TV channels 30 times in a minute. It’s not just a low-level type of curiosity, but instead is a starting point that drives us to seek out new experiences and people and paves the way for two deeper types of curiosity.

Epistemic curiosity manifests when diversive curiosity is honed as a quest for knowledge or understanding. It is “deeper, more disciplined, and effortful” than diversive curiosity, a desire to understand how the world works. Psychologists use the term Need For Cognition (NFC) as a measure of intellectual curiosity. People with a high NFC thrive on and enjoy intellectual challenges, while those low in NFC prefer their mental lives to be as straightforward as possible.

Empathic curiosity is the drive to understand the thoughts and feelings of others, which we can attain by learning to put ourselves in others’ shoes.

A History of Curiosity

Leslie takes us through curiosity’s ups and downs over the past centuries: in some eras, it was looked down upon, and little innovation took place during those times. In other times, for example during the Renaissance, empathic and epistemic curiosity became widely popular, and culture exploded. Cities, too, promote the explosion of curiosity: “The city was a serendipity generator.”

Even now, public opinion of curiosity is a mixed bag: we still repeat warnings of Adam and Eve’s curiosity, we parrot the phrase, “curiosity killed the cat,” use the word curious when we actually mean that someone is weird, and emphasize practical job skills in education over all else. At the same time, there’s a market for books like this one, lauding the trait and going so far as to claim that “your life depends on it.”

How does the Internet fit into society’s curiosity? On the one hand, we have an incredible amount of information literally at our fingertips. Naturally curious people can have a field day, and many do. But people who are lower in NFC can use the internet to stunt the development of their curiosity… which many also do. Who/what/when/where questions can usually be answered by typing a pithy phrase into Google, clicking on the first search result without reading about it, and scanning a sentence or two of the web page. This type of information-seeking is not effortful, and therefore doesn’t engage the processes at work when we truly exercise curiosity. Leslie comes back to this theme often: while the Internet has amazing potential for expanding our horizons and allowing us to share ideas faster than ever, if we’re not careful, it can also squash our curiosity, much to society’s detriment.

Metaphors for curiosity

Puzzle vs. Mystery: Leslie attributes this distinction to security and intelligence expert Gregory Treverton. Some problems are puzzles:

they have definite answers… are orderly; they have a beginning and an eng. Once the missing information is found, it’s not a puzzle anymore. The frustration you felt when you were searching for the answer is replaced by satisfaction. Mysteries are murkier, less neat. They pose questions that can’t be answered definitively, because the answers often depend on a highly complex and interrelated set of factors, both known and unknown… Puzzles tend to be how many or where questions; mysteries are more likely to be why or how.

He uses the question “where is Osama bin Laden?” as an example of a puzzle. Its mystery equivalent might be “how does Osama bin Laden think?” Similarly, reading a mystery novel is also a puzzle, because once you get to the end, you know who did what, and the problem is solved. Reading a novel like The Great Gatsby, on the other hand, is a mystery, because it leaves you thinking about questions that don’t have definite answers, like the true nature of the American dream.

Leslie encourages people to “forage like a foxhog.” This idea, credited to the Greek poet Archilochus, is that “‘The fox knows many things, but the hedgehog knows one big thing.'” The hybrid foxhog is the compromise to the question of whether we should strive to become generally knowledgable people or aim to become experts in very specific areas. The foxhog does both of these, resulting in knowledge that can be considered “T-shaped”: The top of the T is surface knowledge, and foxhogs have a lot of it. The other part of the T is its slender, lengthy spine. Foxhogs also possess tall Ts, because they have intense knowledge about at least one area. In other words, “curious learners go deep, and they go wide.” As a side note, robust, healthy Ts are precisely the goal of a PhD program, designed to make you smart in a way that will be conducive to having happy hour drinks with many people (academics) while becoming so knowledgable about your own field (or subfield, or sub-subfield…) that sometimes you have to teach your advisor what you’re doing.

The Malleability of Curiosity

Leslie emphasizes that “a person’s curiosity is more state than trait.” That means that although we are born with varying degrees of innate NFC, curiosity is highly influenced by our surroundings.

Questions are crucial. They’re tools through which we learn incredible amounts of information about the world.While asking questions may seem like a very basic ability, it actually requires a few important skills: you have to know that there are things you don’t know, you have to be able to imagine that there are different possibilities for the things you don’t know, and you have to recognize that other people are sources of information. A kid between the ages of 2 and 5 will ask roughly 40,000 explanatory questions. And when kids are spoken to by adults who ask questions themselves, the kids begin to ask more. The moral of that story is that asking kids questions gets them to also ask questions, which helps them not only learn about the world, but also to learn that inquiring about the world is a fruitful behavior.

The Importance of Curiosity

Curiosity fosters innovation. Computers are now smarter than humans at many tasks, but computers aren’t curious. For this reason, Leslie writes:

The truly curious will be increasingly in demand. Employers are looking for people who can do more than follow procedures competently or respond to requests, who have a strong, intrinsic desire to learn, solve problems, and ask penetrating questions. They may be difficult to manage at times, there individuals, for their interests and enthusiasms can take them along unpredictable paths, and they don’t respond well to being told what to think. But for the most part, they are worth the difficulty.

Why can curious people innovate better than non-curious ones or better than computers? Curious people are “the ones most likely to make creative connections between different fields, of the kind that lead to new ideas.”

Angela Duckworth is well-known for popularizing the concept of grit: “the ability to deal with failure, overcome setbacks, and focus on long-term goals.” Grit has been demonstrated to be an incredible predictor of success in many areas of life. I once heard two professors talking about the most successful grad students as those who have grit, and their conversation plays through my head on a weekly basis, if not more often. Grit and curiosity go hand in hand. If you’re curious, you just keep learning and exploring, even once you’ve learned what you set out to know. If you’re gritty, you just keep going, even when obstacles arise and the goal you’re pursuing becomes more difficult.

To be curious, you have to know things. One way of thinking about curiosity, attributed to George Loewenstein, is that there’s an information gap: you know some things about a topic, and then realize that you don’t know everything, but that you can learn more. This creates an awesome cycle: the more you learn, the more you want to learn.

What is this thing we call time?

What is this thing we call time?
In English it sits on a line.
How do we know?
Our gestures, they show
Future in front, past behind.

But this is not true for everyone
For Mayans’, word time same as sun
Time revolves like a turn
From which we did learn
Studying time is even more fun!

Image from Walker, E. & Cooperrider, K. (2015). The continuity of metaphor: Evidence from temporal Gestures.

Image from Walker, E. & Cooperrider, K. (2015). The continuity of metaphor: Evidence from temporal Gestures.


Inspired by Le Guen, O. & Pool Balam, L.I. (2012). No metaphorical timeline in gesture and cognition among Yucatec Maya. Frontiers in Psychology, 3: 271. doi:  10.3389/fpsyg.2012.00271