## Monday, December 30, 2013

### Closing Time at the “Astronomy Nightclub”

Today's guest post is from an astronomer friend of mine who moved to the states relatively recently. Her story is not her's alone. I have heard the types of stories she will describe below from many independent sources over the years (dozens, sadly). I have found it increasingly disturbing that certain serial harassers are free to ply their disgusting trade freely within our community. They are not reprimanded because they are highly skilled at covering their tracks and intimidating their victims. Also, universities tend to protect their high-profile professors much quicker than they are to back their younger employees.

Readers: no matter how you might perceive the interactions described below, the key thing to consider is not whether you feel it's right or wrong. Human interactions involve two people, and two sets of experiences and opinions. So please avoid the simple-minded response of, "Jeez, I don't see why this is such a big deal!" in your comments. That response is just not intellectually compelling in my view because it lacks empathy. It takes a lot for a woman in science to speak out about these issues (hence the anonymity of today's guest blogger). I feel it's important for us all to hear them out and try to walk a bit in their shoes rather than sticking to our limited set of personal experiences. Thanks for your attention and thoughtful feedback, dear readers!

In my home country women get remarks on their looks on a daily basis. It happens when they walk down the street, when they are at the grocery store, and sometimes even at work. Here in the US, this doesn't happen so often, and I like it. Especially at work where I can focus on science and nothing else. There is a generally accepted "rule" that we are all scientists in the scientific workplace (for the most part anyway).

Yet, something goes wrong at science conferences; so wrong it tips the balance between men and women in science. It took me a while, with the help of friends, to figure it out (maybe even too long). The rules are subtly different at conferences. What is going on? It makes me wonder: is this a conference or a nightclub?

Here is a situation: at the end of the conference day serval men and a few women (we are often much fewer in number than men) are having dinner and a professional chat. Suddenly one of the women gets a compliment about her eyes (or some such thing).

Wait a minute!

## Thursday, December 19, 2013

### John Cleese on Creativity

My friend and fellow astronomy professor Enrico Ramirez-Ruiz recommended that I do a Google Search for "John Cleese Creativity." I'm so glad I did! The advice offered by Cleese is clear, concise and brilliant:

## Wednesday, December 18, 2013

### Viral post: Is science is in the eye of the beholder? [Hint: NO]

December has proven to be an extremely busy month for me, so with my monthly Women In Astronomy blog post due, I again turned to my friend and colleague Renee Hlozek, a postdoctoral researcher at Princeton University, to write a guest post in my stead. And damn if her post didn't go instantly viral, taking the #4 most-read spot among WIA blog posts by garnering 3600 reads in the past week. So I figured I'd repost it here just in case any of my readers missed it.

Take it away, Renee!

Side note: The past couple months haven't been great for women in science and science journalism. This post links to all the stories of racism and sexism as as experienced by Danielle Lee (#standingwithdnlee!!) and the sexual harassment allegations made by Monica Byrne and Hannah Waters. To be honest, I am pretty overcome by the stories of late. I (like a surprisingly large number of female scientists I know) have experienced sexual harassment, albeit of a rather different kind to that discussed in the articles. While I have lots of thoughts on the pieces, I'm going to save those thoughts for another time and discuss something perhaps a little less obvious. I was actually pretty nervous to discuss even this one for fear of the usual comments it might elicit, but that makes me all the more decided to do so.

We all have bias. If you think you don't, try this eye-opening test on implicit bias from Project Implicit. It'll make you think.

But while we're getting much better on average at identifying obvious forms of bias and sexism (at least I feel there is forward momentum!), one form of sexism is much more subtle: benevolent sexism. Rather than just giving a definition of the term, I'm going to try and relate what happened to me as an example and explain how this well-meaning person made me so angry and frustrated that I had to take a few (many) moments away from my colleagues to calm myself.

## Monday, December 16, 2013

### The Transformative Power of Lying to Yourself

Sarah Rugheimer is a truly amazing person who I've had the great privilege of getting to know better since moving to Harvard. She climbs mountains, collects pond water to teach kids about astrobiology, performs traditional Irish dance, and she also happens to be an outstanding graduate student in the Harvard Astronomy Department working on the remote detection of biomarkers in terrestrial exoplanets. Outside of the department, she is the Director of Communications and Senior Editor of the policylab.org blog. We recently had a conversation about the power of the brain to fool itself into going beyond its limitations, and I asked her to write her experiences with this for a guest post.

I hated my hornpipe dance. I hated everything about it. I hated its music. I hated its rhythm. I hated the dance choreography. But I had to dance it at the qualifier in eight months for the World Irish dance championships. But even the most passionate hate can be transformed to love and success…by lying. This is the story of how I lied to myself long enough to eventually fall in love with my hornpipe. And how I learned lying to oneself is a powerful psychological tool that can lead to success in pursuits far removed from Irish dance, like, for instance, academia.

## Sunday, December 15, 2013

### The NBA's top towel-waver

Every NBA roster has that last dude on the bench. When you think about it, it's not that bad of a job. You get to practice basketball with the best players in the world, you get to travel with the team to various cities, you have one of the best seats in the house, and all without any media pressure and a pretty nice paycheck to boot.

Of course, it's unlikely that many NBA players see things this way. You don't make it to the NBA based on your humility, and the NBA has some of the largest egos in the sports world this side of the NFL.

This is exactly why Kent Bazemore is one of my favorite NBA players. Hailing from Old Dominion University in Norfolk, VA, Bazemore is the backup, backup, backup point guard on the Golden State Warriors (in Oakland, CA), behind Steph Curry, Tony Douglas and Nemanja frikkin' Nedovic. The last time I saw him play was in garbage time with 7 seconds left on the clock, after all of the starters were pulled off the court to standing ovations after a 27-point comeback win against Toronto last Tuesday.

Despite his lowly rank on the Warriors, Bazemore is a big-time player on the bench, where he waves the most effective towel in the league. When Harrison Barnes dunks off a fast-break, there's Bazemore whipping his white towel and yelling like a madman. Ally-oop to Bogut, Bazemore holds the bench back. Hold 'em back, man! When Curry drains a step-back three, Bazemore jumps up, thrusts three fingers in the sky and yells support from the end of the bench. When the other team calls timeout after Klay Thompson sinks consecutive long-balls, Bazemore is the first out on the floor to chest-bump his team mates.

Let's hear it for the Kent Bazemores of the world!

### Snow Shovels

 The SC18PSSPWESH inall it's ergonomic glory
It snowed last night here in Cambridge, MA. It snowed quite a bit. I mean, in a relative sense it snowed infinitely compared to a typical Pasadena, CA snow day, so I might be overreacting a bit. But, wow, there's about a metric shload of snow out there right now.

Thanks to Erin's forethought, we were prepared with two snow shovels, and a scraper for our car windows. We also have a nice metal garden shovel and an old broom. We used all of these implements to ensure compliance with the city's Snow and Ice Removal Ordinance.

On a related note, my back is as old as I am, making my back a 36-year-old back. That's a pretty old back. You know what else? My 36-year-old back hurts quite a bit after shoveling snow all morning. So did Erin's 3N-year-old back (where N is an integer between 0 and 9).

These simple facts inspired us to look into snow-shoveling ergonomics. It turns out that there are, in fact, ergonomic snow shovels, much like the one our neighbor was using this morning. We just purchased a Suncast SC3250 18-inch Poly Snow Shovel Pusher With Ergonomic S-Handle (hereafter the SC18PSSPWESH, for brevity). We'll see how well it works the next time it snows. We also have to wait until it is delivered, which I'm not 100% certain will happen with the roads as they are right now, so here's to better weather in the next 5-7 business days.

Also, I hope that the handle of our SC18PSSPWESH isn't defective like it was for this poor fellow:

I don't know if Carlito "Babushka" Brigante is serious or joking. I don't really think it matters.

## Saturday, December 14, 2013

### 2013 NBA Countdown: #3 Kevin Durant

Kevin Durant (from Texas-Austin) is a scoring monster. He's a two-guard stuck in a small forward's body with the strength of a power forward and the wingspan of a center. He also does it all. He plays smothering defense, rebounds, handles the ball, passes and he's one of the most consistent 20-point scorers in the league.

Owen says (and actually typed this time!), Kevin Durant
1. Does tuff 2's
2. Buries 3's
3. Does layups
4. Puts lots of effort into the game
5. Even #10 in his top 10 is good
6. Sometimes dunks
7. When he does...WATCH OUT!!!

Kevin is also quite stylish

## Wednesday, December 11, 2013

### Intelligence in Astronomy: The Growth of My Intelligence

When I was in graduate school at UC Berkeley, I had a very rough first year. I started astronomy graduate school with a B.S. in physics from a small mid-western school and zero preparation in astronomy. I didn't use a telescope until I was 21 years old, I hadn't taken an astronomy course as an undergrad, and upon my arrival at Berkeley I couldn't tell you why the moon went through phases. Seriously. I learned moon phases as a TA of Astro 10.
 Campbell Hall at UC Berkeley. My office was next to the dome on the right side. The building was torn down a few years ago.
I remember very clearly heading down to the sixth floor of Campbell Hall for my Stellar Structure class, taught by Prof. Frank Shu. As I walked down the stairs with the other students, two of the second-years, Jason Wright and Erik Rosolowsky, were engaged in an intense discussion the likes of which I had never heard before from students. They were discussing whether the forward-scattering of light is the same as inverse Compton scattering, and under which conditions one description is better than the other (or at least this is how I recall the conversation).

I remember chuckling and thinking, "Yeah, right, they're seriously discussing physics outside of class like they're professors. Hilarious!" But then it sank in: they were dead serious. The joke was on me, and it was clear that intellectually I was a long way away from these high-power second-years. They were Smart. I was not.

Interactions like this continued at more or less a steady state in my first year, and I felt less and less capable compared to my peers. Two of my classmates came from Caltech and Harvard, respectively. The third came from Maryland, and he had completed most of his graduate course work as a PhD student there before transferring to Berkeley. I came from the University of Missouri. At Rolla. People generally don't even know how to pronounce Rolla. (BTW, UMR is now Missouri S&T)

The second-years were taking classes with me in the morning, but working on mysterious-looking astronomy data late into the evening and talking in a foreign tongue while doing so. I knew classroom physics, but my fellow students had taken the next step and could describe how all of that problem-set physics applied to things such as interstellar dust grains and the structure of the Sun. I was lost, and I was feeling increasingly stupid.

What saved me was a class taught by a postdoc named Doug Finkbeiner. As a former BADGrad and newly-minted Berkeley postdoc, Doug decided to teach a late-night, unofficial course on astronomical computer programming using this new and exciting scripting language called IDL. About a dozen students gathered in the seventh-floor undergraduate laboratory to use the new Sun workstations that a donor had purchased (oddly, first-year students used Sun SPARC workstations essentially as dummy terminals to login to faster computers. The undergrads had the real computing power with their SunUltra 10s.)

Doug taught a decidedly untraditional class. Each week he would teach a couple new concepts (e.g. array-based math operations), and he'd hand us some data and have us analyze the data using the programming techniques he just described. Because of my extensive past computer science experience I took to the analysis problems like a duck to water. Plus, the vector-based nature of IDL really meshed with the way my brain thinks about the world. Suddenly, all of those classroom lessons on Linear Algebra and even calculus were coming to life on my screen as I manipulated astronomical images. Fourier transforms took on a much deeper meaning after Doug gave us a radio telescope time series of the Crab Pulsar. Not to mention the fact that we were looking at the Crab frikkin' Pulsar!

I took my new-found IDL expertise and applied it to my Stellar Structure and Radiative Processes course work. IDL's plotting tools were just what my visual-manipulation brain needed to see past the confusing, abstract mathematics. Multi-dimensional integration, which to this day is often incomprehensible to me on the written page, became my best friend first using IDL's TOTAL() function and later my own numerical integrators (oooohhhhh, that's how the trapezoidal rule works!).

Doug's fly-by-night class gave me the tools I needed to not just get by in my courses, but start to excel in grad school. HW sets were no longer a slog, but were rather exercises that strengthened my physical intuition, built on my growing problem-solving toolset, and increased my vocabulary. With these traits growing in strength, I was able to start engaging in the science discussions that my older classmates were having, and as I did so my vocabulary, problem-solving sense, and intuition also grew. This positive feedback process led to an exponential growth in all of the traits that eventually led me to become a tenured professor at Harvard.

My intelligence grew exponentially.
 Figure by Nathan Sandars on Astrobites.org. For more see Nathan's interview of yours truly.
More importantly, I didn't quit in the face of difficulty. Why? It's tough to say. I was truly on my way out the door during my first year. One important ingredient was taking a class that was taught in a way that really resonated with the way my brain works. Another ingredient was having Doug Finkbeiner as a teacher. He was, and is, an astrophysical god in my mind, a wizard of the highest order. Yet his approach to science was so humble and down to Earth. He had these sayings that I use to this day. When tackling a tough, complicated problem, he'd say "Just do the stupidest possible thing first." If that doesn't work, start increasing the sophistication slowly until you find a solution. Astrophysical problems can at first seem daunting. But they can all be reduced to sophomore-level physics to get a first-order solution that can be implemented in code. And given the nature of most astrophysical data, first-order is often good enough!

Another ingredient was vividly seeing how the effort I invested paid off in direct proportion in my ability to do astrophysics. More effort in, more growth out. I may not be smart right at this moment, but give me a week or two and I'll be just as smart, if not smarter than you. More importantly, I saw my classmates working their asses off. I figured out that Jason Wright didn't arrive at Berkeley with his encyclopedic knowledge. He arrived with perhaps a quarter of an encyclopedia. At any given moment, I could walk into his office and find him reading an article or book, talking science with his office mates, doing math at his whiteboard, or programming. He worked. Hard. If I wanted to be like Jason, and I most certainly did, I'd have to put in the work.

My intelligence grew exponentially. And it's still growing because when challenges come my way, or when I encounter something that "I should know because I'm a Harvard professor," I don't back down. I don't try to find an easy path around it. I find someone who knows, like Smadar Naoz or Ruth Murray Clay or Avi Loeb, and I ask them "dumb" questions. I ask them to send me to the chalk board so I can struggle through basic problems. And you know what? They don't seem to judge me as being stupid. They help me learn.

At this pace, it's absolutely scary to think of how intelligent I'll be in 10 years.

Epilogue

This year Doug Finkbeiner and I both became tenured professors at Harvard. Doug was tenured jointly in Astronomy and Physics, and I was tenured in Astro. I'm still learning from him and it's an honor to work in the same department as him.

## Tuesday, December 10, 2013

### The simple power of presence in even modest numbers

 Shirley Jackson, the first African-American female Ph.D graduate of MIT. She is now the president of the Rensselaer Polytechnic Institute
Upon arriving in Cambridge I've had the pleasure of getting to know Prof. Chris Rose, who is an engineering professor at Rutgers, currently visiting MIT as an MLK Scholar. We've been talking about diversity in the sciences, with a particular focus on increasing the footprint of what Chris refers to as "the Greater Us," referring to the small community of Black folk among the American science professoriate. Sadly, "small" in this case means epsilon-small.

 Prof. Chris Rose (Rutgers)
Even in 2013, there are only of order 10 Black professors at top-40 astronomy institutions according to this poll taken circa 2007. That's about 1% of all astronomy professors in the US, compared to the 12.6% representation of Blacks in the US population. The same order-of-magnitude discrepancy in representation persists across all science disciplines, from physics to chemistry to comp sci. Decades after the Civil Rights era, the overwhelming majority of all US science professors are white (and male).

That's the bad news. The good news is that increasing the absolute numbers with the addition of ~10 individuals results in a 100% change in the fractional representation of the Greater Us in science in general, and  the astronomy community in particular. And such a change brings benefits that go well beyond the warm fuzzies associated with the mention of progressive concepts of "diversity."

## Monday, December 9, 2013

### Intelligence in Astronomy: Compendium Thus Far

I realize now that I should have referenced previous posts in my Intelligence in Astronomy series in some of my more recent posts. Read in isolation, I suppose my past couple posts would be confusing otherwise. Here is a list of posts so far:

Intelligence: Nature or Nurture? Both, together!
Preview 1 (with some motivation for what follows)
What you think and why it matters
What is Intelligence (Part 1)?
What is Intelligence (Part 2)?
The Growth of my Intelligence
John Cleese on Creativity

Stay tuned for more to come!

### Intelligence in Astronomy: The Fixed Mindset and the Cult of Smart

 Image credit: here
(For my previous posts in this series see this handy compendium. In particular, if you missed it you should check out this post on fixed vs. growth mindsets)

The key feature of a fixed mindset is that intelligence is a fixed, inborn property that does not change in time for a given individual. Those with fixed mindsets tend to see outcomes such as success and failure as a result of these fixed, personal traits. "He didn't get the job because he's not smart" or "I didn't pass the test because I'm not smart" or "she's not a good scientist because she's not smart." I'm sure there are other personal qualities that people could focus on other than smartness, but in the realm of science for many people with fixed mindsets it comes down to who is and is not "smart."

I like to refer to this fixation on smartness as the Cult of Smart. Somewhat pejorative? Yes, indeed. Apropos? Big-time. Primarily because this stance is based more on faith than scientific evidence.

Members of the Cult of Smart can be found in all astronomy departments and, sadly, their voices are
 "Nope! Not good enough." says Prof. Cowell thoughtfully
quite loud. Whereas other people try to used nuance in explaining why others are excellent, or in predicting future success, members of the Cult are certain in their black-and-white evaluations. They're quick to use short, few-sentence evaluations that not only convey their point of view, but also tend to squelch further discussion by making everyone else in the room feel insecure.

"How about candidate A? He had a really interesting recent result that I---"

"NOPE! Not good. Doesn't even know GR. No way, not good enough."

"But, he gave a great talk at a recent conference I was at, and he really showed a deep understanding during the Q&A. What I particularly liked was---"

"Oh, come on! Seriously? Are you crazy? Where's the fundamental physics? This guy doesn't know anything."

(Note that this is not an actual conversation, but it is based on many real conversations I've been involved in over the years. It's similarity to the opinions of specific individuals is purely coincidental, but not unlikely.)

Why do people act this way? Well, think about their fixed mindset. To their minds intelligence is a fixed trait, and guess who has it? They do! It's their birthright and what sets them apart from everyone else. They're special, and there are only a chosen few who are like them. Unless the person they are evaluating exhibits the signs of the excellence they see in themselves, then those people aren't useful for much. They're not good now and they're not gonna be any better in the future.

Keep in mind that this is just one, particularly pathological manifestation of a fixed mindset. Even if you aren't a pompous blow-hard, as I'm sure you, dear reader, are not, you can still suffer from other side effects of a fixed outlook. If you believe that intelligence is innate and immutable, then what does facing an intellectual challenge tell you? If you're stuck working a tough problem, then what's the message? The message is fairly clear and not very positive: This is as far as you can go. You're not that smart after all.

After hitting this point and having those types of thoughts, students often drop out of contact with their advisors. "Oh no! I can't let the Prof. know that I'm stuck. She'll think I'm an idiot. Maybe I am an idiot! No! I can't let her know. Maybe if I sit here for a month or two the answer will drop from the sky. Maybe my intelligence is just temporarily suspended somehow." Weeks go by and the professor starts wondering where that bright-eyed, young student research went. Did they run off and join the Peace Corps? Did they get hit by a train?

This phenomenon of the disappearing student occurs regularly even at the most elite universities and research institutions. It happens with students who are smart by traditional definitions, and those who are not. It happens for students with good grades and bad, both high and low GRE scores.

Another manifestation of fixedness is a difficulty in taking criticism, whether constructive or not. This is how my fixed attitudes are manifested. When someone gives me specific yet critical feedback, rather than taking it for what it is---advice on how I can improve one specific aspect of my research or personal behavior---I sometimes take it as a global assessment of my self-worth.

Person: "You know, it would help to include a few more references on your slides."

Me thinking to myself: "What?! Doesn't this person realize how much work I put into this talk. Just who do they think they are, telling me that I don't give good talks. I cite so many people in my talks. Are they saying I'm a self-centered, selfish, ungrateful person? How dare they! Screw them and their crappy advice!"

 A Smart crashing. Credit here
Okay, this is a little exaggerated. And to my credit, I've been consciously working on this over the past few years. I've even come up with a standard set of responses that I practice saying ahead of time. Things like, "Thank you very much for that feedback. I can see what you mean and I also see how that will improve my talk in the future." I'm actively countering my fixed mentality in certain areas.

But what about those judgmental people? Are they judging you as being smart or not smart right now? Yup, they sure are and they're not likely to go anywhere anytime soon. This is something that we all have to deal with, just like we have to deal with rainy days, bad drivers, rude people at the grocery store, crying babies on airplanes, etc. It's a part of life in general, and academic life in particular, that we'll be judged rather frequently, often by people with fixed mindsets.

The only question is: How will you deal with it. Is your identity and worth tied up in others reaffirming your inborn talent and intelligence? If so, it'll be rough for you in the coming years as those fixed-minded individuals in higher positions pass judgement on you. You'll waste precious brain-CPU cycles ruminating on what others think about you and your smartness. Every comment sent your way will run through a filter that transforms off-handed remarks into judgements of your personal worth. You'll have a tough time.

Or will you overcome your fixed-mindset tendencies and start marginalizing out the pompous blow-hards and start working with me to form a vocal contingent to push back on the old ways of thinking?

## Thursday, December 5, 2013

### Intelligence in Astronomy: What Is Intelligence? (Part 2)

One night in Cambridge, England in the late 1970's, two astrophysics postdocs were sitting at a table outside of the Ft. Saint George Pub. One of the astrophysicists was Ed Turner (Princeton) and the other was Scott Tremaine (Institute for Advanced Study). As my good friend, Ed Turner, tells the story
At some point we fell to debating which of our famous senior colleagues was the best scientist.  Ostriker, Rees, Peebles, Lynden-Bell and others appeared in the conversation. We failed to find a compelling case for any one of them or even for comparing any two of them; generally there were arguments for many or both alternatives re who was the best.  I can't recall whether we discussed only theorists or also some observers.
Anyway, at some point we noticed that while it was very hard to say whether X was better than Y or vice versa as an overall scientist, it was often relatively easy to say which was better at some particular aspect of science...like who had the most extensive and detailed knowledge or who was more creative or who picked the best problems etc.  I recall making some analogy to comparing baseball players; it is hard to say who is the best overall but relatively easy to say who has the highest batting average, hits the most HRs, steals the most bases etc...
From this point it was only a short hop into science nerdery as they imagined the various components of excellence and traits of successful astronomers as basis vectors in a multidimensional hyperspace, which they termed the 7-Dimensional Scientist Hyperspace (7DSH, pronounced "seven-dish," I guess :). The seven dimensions of excellence that they identified was some version of the following according to an email Scott Tremaine sent me in response to my inquiry:

Taste  - Ability to identify an important question that can be addressed with the skills that you possess.
Intelligence -   Adeptness at the basic problem solving, calculating, perceptual skills needed to work the problem.
Grit - Ability to do the hard extended work needed.  Ability to maintain attention.  Ability to complete. The ability to face struggles and push through.
Knowledge - Breadth and extent of the corpus of knowledge needed to solve the problem and bring in interesting external information.
Curiosity - Alertness to interesting paths, byways, anomalies, etc.
Luck - Intuitive ability to expose oneself to, select for, and respond to constructive paths.

I really like these dimensions. Note, however, that they do not necessarily form an orthogonal basis set. One cannot be lucky or creative or curious without gaining the necessary knowledge. One cannot communicate well without good taste in selecting the right questions.

Note also that these traits are not static qualities of an individual, and smartness is only one component of success (mostly closely aligned with a combination of knowledge and intelligence). Even if you don't think you are getting smarter in time, and many people doubt that they are, one's knowledge increases monotonically throughout their lives, curiosity comes through effective communication with others, which generates ideas that can lead to asking important new questions. First-year students don't arrive on campus with this sort of software bundled and pre-installed. These are things that need to be learned, and successful graduate programs focus on training students and helping their projections in these various dimensions grow in time.

 Moving from one-dimensional "smartness" to multi-dimensional excellence
After thinking on 7DSH for a few months now, I've devised my modified 7-dimensional hyperspace of scientific excellence (M7DHSE), which draws upon the Turner & Tremaine conception as well as Sternberg's Successful Intelligence:

Creativity - The ability to successfully deal with new and unusual research problems and situations by drawing on existing knowledge and skills. The ability to connect disparate concepts to devise solutions to outstanding problems.

Curiosity - Alertness to interesting paths, byways, anomalies. The ability to identify important questions that can be addressed with one’s skills.

Basic intelligence - The ability to quickly identify the correct solution to academic, problem-solving tasks by drawing upon fundamental physical concepts.

Knowledge - Breadth and depth of the corpus of information one possesses that can be used to solve problems

Productivity - The ability to understand what needs to be done in a specific setting and then do it at  rate that contributes to the advancement of knowledge throughout one’s field

Communication The ability to advance ideas; generate needed input through positive interactions  with others; and disseminate results in oral and/or written form so that others can use them to advance the field.

Pedagogy - Abilities related to the effective training of the next generation of excellent scientists through teaching, advising and mentoring. The ability to adapt to different backgrounds and learning styles in order to help others learn how to be excellent

How does your ability vector, $\vec{A}$,  project into this hyperspace? What is the magnitude of your vector, $|\vec{A}|$? An most importantly, what is the time derivative of your vector, $d\vec{A}/dt$ and what are you doing to accelerate that growth?

## Tuesday, December 3, 2013

### Intelligence in Astronomy: What Is Intelligence? (Part 1)

In my previous post we saw how you, the readers of this blog, see intelligence as a key to success. The vast majority of you also see intelligence as something that can increase in time. But a question was left lingering: What do we mean by intelligence?

Is intelligence encapsulated in standardized tests? How about the IQ test? Here's what a famous psychologist, Alfred Binet, had to say about the IQ test:
“The scale [the IQ test], properly speaking, does not permit the measure of the intelligence, because intellectual qualities are not superposable, and therefore cannot be measured as linear surfaces are measured.”
"With practice, training, and above all method, we manage to increase our attention, our memory, our judgement and literally become more intelligent than we were before."

## Monday, December 2, 2013

### Jason Wright on Waste Heat and Avoiding Malthus' Prediction

Prof. Jason Wright (PSU) is searching for evidence of galaxy-wide alien civilizations who have avoided Malthusian catastrophes by constructing circumstellar swarms solar arrays. Here's an excellent, TED-style talk of how he and his group are doing it. For more, check out his series of blog posts on his SETI project.

If you're looking for a well-delivered, through-provoking colloquium talk for next year, you should drop Jason a line.

## Sunday, December 1, 2013

### Things are looking up. Really, they are!

Prof. Turner sent a link to this article that gives 23 plots that you should find encouraging. Things are looking up compared to the past. Science is improving and lengthening our lives while decreasing the likelihood of dying from things such as malaria. Speaking of, here's one of the 23 encouraging plots:

When governments spend money on research, the investment pays off. Money in leads to more money out in the form of decreased spending on treating, e.g. malaria. Science FTW!

### Intelligence in Astronomy: What You Think and Why It Matters

In astronomy we are acutely aware of the importance of intelligence for success in science. Last week I posted a poll to gauge the opinions on intelligence of the readers of this blog, and most of you are current or former astronomers/physicist or other scientists, or know astronomers/physicists or other scientists. The first question (statement):
The vast majority of you strongly agree with the notion that intelligence is important for success in science. But what do I mean by intelligence? That's a hugely important question, but I left the definition out of this question for a reason. Back to this in a bit.

The next statement:

This is rather remarkable to me. However you readers define intelligence, the majority of you feel that it is a mutable quality of an individual. More specifically, you all feel that it is something that is not a fixed trait. Can it increase in time?
The readers of this blog not only believe that intelligence can change over the course of a person's life, but they also strongly believe that intelligence can increase in time as a result of diligent practice. This is remarkable!

So what's up with these questions? Well, it turns out that these questions are key diagnostics of what psychologist Carol Dweck and collaborators call "mindset." In their extensive studies over the past 30 years they have found that humans exhibit two broad classes of mindset: fixed and growth-oriented (see  Dweck's highly accessible book Mindset, and/or Dweck, Chiu & Hong 1995).

People with a fixed mindset believe, either implicitly or explicitly, that the qualities of a person are fixed, perhaps even at birth, and these traits do not change appreciably over time. The other mindset is focused on growth. Growth-oriented people see intelligence as a malleable property of a person, and more importantly that it can increase in time.

It is important to note that there are plenty of examples of successful scientists with both types of mindsets. Fixed and Growth minded individuals are hired into faculty positions at top institutions, win grants, garner awards and attain success measured by numerous metrics.

However, the two mindsets lead to stark differences in
1. the ways in which people interact with others within their scientific communities
2. attitudes towards and quality of teaching
3. the reactions that individuals have toward adversity
4. openness to change, notably when it comes to issues related to diversity and inclusion
In the posts to come, I'll delve into issues related to mindset and intelligence. Next up: what do we mean by "intelligence?"