Book Club: The 10,000 Year Explosion: pt 4 Agriculture

Welcome back to EvX’s Book Club. Today we’re discussing Chapter 4 of The 10,000 Year Explosion: Consequences of Agriculture.

A big one, of course, was plague–on a related note, Evidence for the Plague in Neolithic Farmers’ Teeth:

When they compared the DNA of the strain recovered from this cemetery to all published Y. pestis genomes, they found that it was the oldest (most basal) strain of the bacterium ever recovered. Using the molecular clock, they were able to estimate a timeline for the divergence and radiation of Y. pestis strains and tie these events together to make a new, testable model for the emergence and spread of this deadly human pathogen.

These analyses indicate that plague was not first spread across Europe by the massive migrations by the Yamnaya peoples from the central Eurasian steppe (around 4800 years ago)… Rascovan et al. calculated the date of the divergence of Y. pestis strains at between 6,000 and 5,000 years ago. This date implicates the mega-settlements of the Trypillia Culture as a possible origin point of Y. pestis. These mega-settlements, home to an estimated 10,000-20,000 people, were dense concentrations of people during that time period in Europe, with conditions ideal for the development of a pandemic.

The Cucuteni-Trypilia Culture flourished between the Carpathian Mountains and the Black Sea from 4800-3000 BC. It was a neolithic–that is, stone age–farming society with many large cities. Wikipedia gives a confused account of its demise:

According to some proponents of the Kurgan hypothesis of the origin of Proto-Indo-Europeans … the Cucuteni–Trypillia culture was destroyed by force. Arguing from archaeological and linguistic evidence, Gimbutas concluded that the people of the Kurgan culture (a term grouping the Yamnaya culture and its predecessors) … effectively destroyed the Cucuteni–Trypillia culture in a series of invasions undertaken during their expansion to the west. Based on this archaeological evidence Gimbutas saw distinct cultural differences between the patriarchal, warlike Kurgan culture and the more peaceful egalitarian Cucuteni–Trypillia culture, … which finally met extinction in a process visible in the progressing appearance of fortified settlements, hillforts and the graves of warrior-chieftains, as well as in the religious transformation from the matriarchy to patriarchy, in a correlated east–west movement.[26] In this, “the process of Indo-Europeanization was a cultural, not a physical, transformation and must be understood as a military victory in terms of successfully imposing a new administrative system, language, and religion upon the indigenous groups.[27]

How does it follow that the process was a cultural, not physical transformation? They got conquered.

In his 1989 book In Search of the Indo-Europeans, Irish-American archaeologist J. P. Mallory, summarising the three existing theories concerning the end of the Cucuteni–Trypillia culture, mentions that archaeological findings in the region indicate Kurgan (i.e. Yamnaya culture) settlements in the eastern part of the Cucuteni–Trypillia area, co-existing for some time with those of the Cucuteni–Trypillia.[4]Artifacts from both cultures found within each of their respective archaeological settlement sites attest to an open trade in goods for a period,[4] though he points out that the archaeological evidence clearly points to what he termed “a dark age,” its population seeking refuge in every direction except east. He cites evidence of the refugees having used caves, islands and hilltops (abandoning in the process 600–700 settlements) to argue for the possibility of a gradual transformation rather than an armed onslaught bringing about cultural extinction.[4]

How is “refugees hiding in caves” a “gradual transformation?” That sounds more like “people fleeing an invading army.”

The obvious issue with that theory is the limited common historical life-time between the Cucuteni–Trypillia (4800–3000 BC) and the Yamnaya culture (3300–2600 BC); given that the earliest archaeological findings of the Yamnaya culture are located in the VolgaDonbasin, not in the Dniester and Dnieper area where the cultures came in touch, while the Yamnaya culture came to its full extension in the Pontic steppe at the earliest around 3000 BC, the time the Cucuteni–Trypillia culture ended, thus indicating an extremely short survival after coming in contact with the Yamnaya culture.

How is that an issue? How long does Wikipedia think it takes to slaughter a city? It takes a few days. 300 years of contact is plenty for both trade and conquering.

Another contradicting indication is that the kurgans that replaced the traditional horizontal graves in the area now contain human remains of a fairly diversified skeletal type approximately ten centimetres taller on average than the previous population.[4]

What are we even contradicting? Sounds like they got conquered, slaughtered, and replaced.

Then Wikipedia suggests that maybe it was all just caused by the weather (which isn’t a terrible idea.) Drought weakened the agriculturalists and prompted the pastoralists to look for new grasslands for their herds. They invaded the agriculturalists’ areas because they were lush and good for growing grain, which the pastoralists’ cattle love eating. The already weakened agriculturalists couldn’t fight back.

ANYWAY. Lets get on with Greg and Henry’s account, The 10,000 Year Explosion:

The population expansion associated with farming increased crowding, while farming itself made people sedentary. Mountains of garbage and water supplies contaminated with human waste favored the spread of infectious disease. …

Most infectious diseases have a critical community size, a  number and concentration of people below which they cannot persist. The classic example is measles, which typically infects children and remains infectious for about ten days, after which the patient has lifelong immunity. In order for measles to survive, the virus that causes it, the paramyxovirus, must continually find unexposed victims–more children. Measles can only persist in a large, dense population: Populations that are too small or too spread out (under half a million in close proximity) fail to produce unexposed children fast enough, so the virus dies out.

Measles, bubonic plague, smallpox: all results of agriculture.

Chickenpox: not so much.

I wonder if people in the old Cucuteni–Trypillia area are particularly immune to bubonic plague, or if the successive waves of invading steppe nomads have done too much genetic replacement (slaughtering) for adaptations to stick around?

Harpending and Cochran then discuss malaria, which has had a big impact on human genomes (eg, sickle cell,) in the areas where malaria is common.

In general, the authors play it safe in the book–pointing to obvious cases of wide-scale genetic changes like sickle cell that are both undoubtable and have no obvious effect on personality or intelligence. It’s only in the chapter on Ashkenazi IQ that they touch on more controversial subjects, and then in a positive manner–it’s pleasant to think, “Why was Einstein so smart?” and less pleasant to think, “Why am I so dumb?”

However:

It’s time to address the old chestnut that biological differences among human populations are “superficial,” only skin-deep. It’s not true: We’re seeing genetically caused differences in all kinds of functions, and every such differences was important enough to cause a significant increase in fitness (number of offspring)–otherwise it wouldn’t have reached high frequency in just a few millennia.

As for skin color, Cochran and Harpending lean on the side of high-latitude lightening having been caused by agriculture, rather than mere sunlight levels:

Interestingly, the sets of changes driving light skin color in China are almost entirely different from those performing a similar function in Europe. …

Many of these changes seem to be quite recent. The mutation that appears to have the greatest effect on skin color among Europeans and neighboring peoples, a variant of SLC24A5, has spread with astonishing speed. Linkage disequilibrium… suggests that it came into existence about 5,800 years ago, but it has a frequency of 99 percent throughout Europe and is found at significant levels in North Africa, East Africa, and as far east as India and Ceylon. If it is indeed that recent, it must have had a huge selective advantage, perhaps as high as 20 percent. It would have spread so rapidly that, over a long lifetime a farmer could have noticed the change in appearance in his village.

Wow.

In humans, OAC2 … is a gene involved in the melanin pathway… Species of fish trapped in caves… lose their eyesight and become albinos over many generations. … Since we see changes in OCA2 in each [fish] case, however, there must have been some advantage in knocking out OCA2, at least in that underground environment. The advantage cannot like in increased UV absorption, since there’s no sunlight in those caves.

There are hints that knocking out OCA2, or at least reducing its activity, may he advantageous… in humans who can get away with it. We see a pattern that suggests that having one inactive copy of OCA2 is somehow favored even in some quite sunny regions. In southern Africa, a knocked-out version of OCA2 is fairly common: The gene frequency is over 1 percent.

And that’s an area with strong selection for dark skin.

A form of OCA2 albinism is common among the Navajo and other neighboring tribes, with gene frequencies as high as 4.5 percent. The same pattern appears in southern Mexico, eastern Panama, and southern Brazil. All of which suggests that heterozygotes…may ave some advantage.

Here is an article on the possibility of sexual selection for albinism among the Hopi.

So why do Europeans have such variety in eye and hair color?

Skeletons

The skeletal record clearly supports the idea that there has been rapid evolutionary change in humans over the past 10,000 years. The human skeleton has become more gracile–more lightly built–though more so in some populations than others. Our jaws have shrunk, our long bones have become lighter, and brow ridges have disappeared in most populations (with the notable exception of Australian Aborigines, who have also changed, but not as much; they still have brow ridges, and their skulls are about twice as thick as those of other peoples.)

This could be related to the high rates of interpersonal violence common in Australia until recently (thicker skulls are harder to break) or a result of interbreeding with Neanderthals and Denisovans. We don’t know what Denisovans looked like, but Neanderthals certainly are noted for their robust skulls.

Skull volume has decreased, apparently in all populations: In Europeans, volume is down about 10 percent from the high point about 20,000 years ago.

This seems like a bad thing. Except for mothers.

Some changes can be seen even over the past 1,000 years. English researchers recently compared skulls from people who died in the Black Death ([approximately] 650 years ago), from the crew of the Mary Rose,a  ship that sank in Tudor times ([approximately] 450 years ago) and from our contemporaries. The shape of the skull changed noticeably over that brief period–which is particularly interesting because we know there has been no massive population replacement in England over the past 700 years.

Hasn’t there been a general replacement of the lower classes by the upper classes? I think there was also a massive out-migration of English to other continents in the past five hundred years.

The height of the cranial vault of our contemporaries was about 15 percent larger than that of the earlier populations, and the part of the skull containing the frontal lobes was thus larger.

This is awkwardly phrased–I think the authors want the present tense–“the cranial vault of our contemporaries is…” Nevertheless, it’s an interesting study. (The frontal lobes control things like planning, language, and math.) 

We then proceed to the rather depressing Malthus section and the similar “elites massively out-breeding commoners due to war or taxation” section. You’re probably familiar with Genghis Khan by now. 

We’ve said that the top dogs usually had higher-than-average fertility, which is true, but there have been important exceptions… The most common mistake must have been living in cities, which have almost always been population sinks, mostly because of infectious disease. 

They’re still population sinks. Just look at Singapore. Or Tokyo. Or London. 

The case of silphium, a natural contraceptive and abortifacient eaten to extinction during the Classical era, bears an interesting parallel to our own society’s falling fertility rates. 

And of course, states domesticate their people: 

Farmers don’t benefit from competition between their domesticated animals or plants… Since the elites were in a very real sense raising peasants, just as peasants raised cows, there must have been a tendency for them to cull individuals who were more aggressive than average, which over time would have changed the frequencies of those alleles that induced such aggressiveness.

On the one hand, this is a very logical argument. On the other hand, it seems like people can turn on or off aggression to a certain degree–uber peaceful Japan was rampaging through China only 75 years ago, after all. 

Have humans been domesticated? 

(Note: the Indians captured by the Puritans during the Pequot War may have refused to endure the yoke, but they did practice agriculture–they raised corn, squash and beans, in typical style. Still, they probably had not endured under organized states for as long as the Puritans.)

There is then a fascinating discussion of the origins of the scientific revolution–an event I am rather fond of. 

Although we do not as yet fully understand the true causes of the scientific and industrial revolution, we must now consider the possibility that continuing human evolution contributed to that process. It could explain some of the odd historical patterns that we see.

Well, that’s enough for today. Let’s continue with Chapter 5 next week.

How about you? What are your thoughts on the book?

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A Little Review of Big Data Books

I recently finished three books on “big data”– Big Data: A Revolution That Will Transform How We Live, Work, and Think, by Viktor Mayer-Schönberger and Kenneth Cukier; Everybody Lies: Big Data, New Data, and What the Internet can tell us about who we Really Are, by Seth Stephens-Davidowitz; and Big Data At Work: Dispelling the Myths, Uncovering the opportunities, by Thomas H. Davenport.

None of these books was a whiz-bang thriller, but I enjoyed them.

Big Data was a very sensible introduction. What exactly is “big data”? It’s not just bigger data sets (though it is also that.) It’s the opportunity to get all the data.

Until now, the authors point out, we have lived in a data poor world. We have had to carefully design our surveys to avoid sampling bias because we just can’t sample that many people. There’s a whole bunch of math done over in statistics to calculate how certain we can be about a particular result, or whether it could just be the result of random chance biasing our samples. I could poll 10,000 people about their jobs, and that might be a pretty good sample, but if everyone I polled happens to live within walking distance of my house, is this a very representative sample of everyone in the country? Now think about all of those studies on the mechanics of sleep done on whatever college students or homeless guys a scientist could convince to sleep in a lab for a week. How representative are they?

Today, though, we suddenly live in a data rich world. An exponentially data rich world. A world in which we no longer need to correct for bias in our sample, because we don’t have to sample. We can just get… all the data. You can go to Google and find out how many people searched for “rabbit” on Tuesday, or how many people misspelled “rabbit” in various ways.

Data is being used in new and interesting (and sometimes creepy) ways. Many things that previously weren’t even considered data are now being quantitized–like one researcher quantitizing people’s backsides to determine whether a car is being driven by its owner, or a stranger.

One application I find promising is using people’s searches for various disease symptoms to identify people who may have various diseases before they seek out a doctor. Catching cancer patients earlier could save millions of lives.

I don’t have the book in front of me anymore, so I am just going by memory, but it made a good companion to Auerswald’s The Code Economy, since the modern economy runs so much on data.

Everybody Lies was a much more lighthearted, annecdotal approach to the subject, discussing lots of different studies. Davidowitz was inspired by Freakonomics, and he wants to use Big Data to uncover hidden truths of human behavior.

The book discusses, for example, people’s pornographic searches, (as per the title, people routinely lie about how much porn they look at on the internet,) and whether people’s pornographic preferences can be used to determine what percent of people in each state are gay. It turns out that we can get a break down of porn queries by state and variety, allowing a rough estimate of the gay and straight population of each state–and it appears that what people are willing to tell pollsters about their sexuality doesn’t match what they search for online. In more conservative states, people are less likely to admit to pollsters that they are gay, but plenty of supposedly “straight” people are searching for gay porn–about the same number of people as actually admit to being gay in more liberal states.

Stephens-Davidowitz uses similar data to determine that people have been lying to pollsters (or perhaps themselves) about whom they plan to vote for. For example, Donald Trump got anomalously high votes in some areas, and Obama got anomalously low votes, compared to what people in those areas told pollsters. However, both of these areas correlated highly with areas of the country where people made a lot of racist Google searches.

Most of the studies discussed are amusing, like the discovery of the racehorse American Pharaoh. Others are quite important, like a study that found that child abuse was probably actually going up at a time when official reports said it wasn’t–the reports probably weren’t showing abuse due to a decrease in funding for investigating abuse.

At times the author steps beyond the studies and offers interpretations of why the results are the way they are that I think go beyond what the data tells, like his conclusion that parents are biased against their daughters because they are more concerned with girls being fat than with boys, or because they are more likely to Google “is my son a genius?” than “is my daughter a genius?”

I can think of a variety of alternative explanations. eg, society itself is crueler to overweight women than to overweight men, so it is reasonable, in turn, for parents to worry more about a daughter who will face cruelty than a boy who will not. Girls are more likely to be in gifted programs than boys, but perhaps this means that giftedness in girls is simply less exceptional than giftedness in boys, who are more unusual. Or perhaps male giftedness is different from female giftedness in some way that makes parents need more information on the topic.

Now, here’s an interesting study. Google can track how many people make Islamophobic searches at any particular time. Compared against Obama’s speech that tried to calm outrage after the San Bernardino attack, this data reveals that the speech was massively unsuccessful. Islamophobic searches doubled during and after the speech. Negative searches about Syrian refugees rose 60%, while searches asking how to help dropped 35%.

In fact, just about every negative search we cold think to test regarding Muslims shot up during and after Obama’s speech, and just about every positive search we could think to test declined. …

Instead of calming the angry mob, as everybody thought he was doing, the internet data tells us that Obama actually inflamed it.

However, Obama later gave another speech, on the same topic. This one was much more successful. As the author put it, this time, Obama spent little time insisting on the value of tolerance, which seems to have just made people less tolerant. Instead, “he focused overwhelmingly on provoking people’s curiosity and changing their perceptions of Muslim Americans.”

People tend to react positively toward people or things they regard as interesting, and invoking curiosity is a good way to get people interested.

The author points out that “big data” is most likely to be useful in fields where the current data is poor. In the case of American Pharaoh, for examples, people just plain weren’t getting a lot of data on racehorses before buying and selling them. It was a field based on people who “knew” horses and their pedigrees, not on people who x-rayed horses to see how big their hearts and lungs were. By contrast, hedge funds investing in the stock market are already up to their necks in data, trying to maximize every last penny. Horse racing was ripe for someone to become successful by unearthing previously unused data and making good predictions; the stock market is not.

And for those keeping track of how many people make it to the end of the book, I did. I even read the endnotes, because I do that.

Big Data At Work was very different. Rather than entertain us with the success of Google Flu or academic studies of human nature, BDAW discusses how to implement “big data” (the author admits it is a silly term) strategies at work. This is a good book if you own, run, or manage a business that could utilize data in some way. UPS, for example, uses driving data to minimize package delivery routes; even a small saving per package by optimizing routes leads to a large saving for the company as a whole, since they deliver so many packages.

The author points out that “big data” often isn’t big so much as unstructured. Photographs, call logs, Facebook posts, and Google searches may all be “data,” but you will need some way to quantitize these before you can make much use of them. For example, companies may want to gather customer feedback reports, feed them into a program that recognizes positive or negative language, and then quantitizes how many people called to report that they liked Product X vs how many called to report that they disliked it.

I think an area ripe for this kind of quantitization is medical data, which currently languishes in doctors’ files, much of it on paper, protected by patient privacy laws. But people post a good deal of information about their medical conditions online, seeking help from other people who’ve dealt with the same diseases. Currently, there are a lot of diseases (take depression) where treatment is very hit-or-miss, and doctors basically have to try a bunch of drugs in a row until they find one that works. A program that could trawl through forum posts and assemble data on patients and medical treatments that worked or failed could help doctors refine treatment for various difficult conditions–“Oh, you look like the kind of patient who would respond well to melatonin,” or “Oh, you have the characteristics that make you a good candidate for Prozac.”

The author points out that most companies will not be able to keep the massive quantities of data they are amassing. A hospital, for example, collects a great deal of data about patient’s heart rates and blood oxygen levels every day. While it might be interesting to look back at 10 years worth of patient heart rate data, hospitals can’t really afford to invest in databanks to store all of this information. Rather, what companies need is real-time or continuous data processing that analyzes current data and makes predictions/recommendations for what the company (or doctor) should do now.

For example, one of the books (I believe it was “Big Data”) discussed a study of premature babies which found, counter-intuitively, that they were most likely to have emergencies soon after a lull in which they had seemed to be doing rather well–stable heart rate, good breathing, etc. Knowing this, a hospital could have a computer monitoring all of its premature babies and automatically updating their status (“stable” “improving” “critical” “likely to have a big problem in six hours”) and notifying doctors of potential problems.

The book goes into a fair amount of detail about how to implement “big data solutions” at your office (you may have to hire someone who knows how to code and may even have to tolerate their idiosyncrasies,) which platforms are useful for data, the fact that “big data” is not all that different from standard analytics that most companies already run, etc. Once you’ve got the data pumping, actual humans may not need to be involved with it very often–for example you may have a system that automatically updates drives’ routes with traffic reports, or sprinklers that automatically turn on when the ground gets too dry.

It is easy to see how “big data” will become yet another facet of the algorithmization of work.

Overall, Big Data at Work is a good book, especially if you run a company, but not as amusing if you are just a lay reader. If you want something fun, read the first two.

Homeschooling Corner: Science (geology and geography)

 

I have yet to find any “science kits” that actually teach science–most are just science-themed toys. There’s nothing wrong with that, but don’t expect your kid to re-derive the principles of chemistry via a baking soda volcano.

Smaller kids aren’t ready for the kind of thinking required for actual scientific research, but they can still learn plenty of science the mundane way: by reading. So here are some of our favorite science books/activities:

We did geology over the winter, centered around Rocks, Rivers, and the Changing Earth. It’s a lovely book (reading level about second grade?) with instructions for many simple experiments (eg, put rocks, sand, water in a glass jar and carefully shake/swirl to observe the effects of different water speeds on riverbanks) and handily complements any nature walks, rock collecting trip, or expeditions to the seashore.

WARNING: This book was published before plate tectonics became widely accepted and so has a confused chapter or two on how mountains form. SKIP THIS CHAPTER.

We also tried making polished stones in a rock tumbler (verdict: not worth the cost.)

After geology, we transitioned to geography with A Child’s Introduction to the World: Geography, Cultures and People–from the Grand Canyon to the Great Wall of China. I admit that geography sounds more like social studies than science, but it flows so perfectly from our understanding of geology that I have to mention it here.

I like to read this with a globe and children’s atlas at hand, so I can easily demonstrate things like latitude and longitude, distances, and different map projections.

With spring’s arrival we also began a study of plants and insects.

If you’ve never started your own plants from seed, any common crop seeds sold at the store–beans, peas, corn, squash, and most flowers–will sprout quickly and easily. If you want to keep your plants indoors, I recommend you get a bag of dirt at the garden center. This dirt is supposed to be “clean”; the dirt found outside in your yard is full of bugs that you probably weren’t intending on studying in your living room.

Speaking of bugs, we bought the “raise your own ladybugs” and butterflies kits, but I don’t recommend these as real caterpillars are nowhere near as cute and interesting as the very hungry one in the story. I think you’re better off just collecting ladybugs in the wild and reading about them at home.

The Way Things Work (also by this author: How Machines Work: Zoo Break) This is a big, beautiful book aimed at older kids, maybe about 10+. Younger kids can enjoy it if you read it with them.

Super Science: Matter Matters is a fabulous pop-up/lift-the-flap book about chemistry. We were very lucky to receive this as a birthday gift. (Birthday hint: the homeschooling families in your life would always like more books.) The book is a little fragile, so not appropriate for younger children who might pull too hard on the tabs, but great for everyone else.

Magic Schoolbus anything. There are probably several hundred books in this series by now. Who Was Albert Einstein? We finished our math biographies, so on to science bios. Basher Science: Astronomy  This is cute, and there are a bunch in the series. I’m looking forward to the rest. Professor Astro Cat‘s Atomic Adventure (also, Space!)

Come read “The Code Economy: A 40,00 Year History” with us

I don’t think the publishers got their money’s worth on cover design

EvX’s Book Club is reading Philip Auerswald’s The Code Economy: A 40,000 Year History looks at how everything humans produce, from stone tools to cities to cryptocurrencies like bitcoin, requires the creation, transmission, and performance of “code,”  and explores the notion that human societies–and thus civilization–is built on a mountain of of encoded processes.

I loved this book and am re-reading it, so I would like to invite you to come read it, too.

Discussion of Chapter 1 Jobs: Divide and Coordinate, will begin on May 23 and last as long as we want it to.

Here’s Amazon’s blurb about the book:

What do Stone Age axes, Toll House cookies, and Burning Man have in common? They are all examples of code in action.

What is “code”? Code is the DNA of human civilization as it has evolved from Neolithic simplicity to modern complexity. It is the “how” of progress. It is how ideas become things, how ingredients become cookies. It is how cities are created and how industries develop.

In a sweeping narrative that takes readers from the invention of the alphabet to the advent of the Blockchain, Philip Auerswald argues that the advance of code is the key driver of human history. Over the span of centuries, each major stage in the advance of code has brought a shift in the structure of society that has challenged human beings to reinvent not only how we work but who we are.

We are in another of those stages now. The Code Economy explains how the advance of code is once again fundamentally altering the nature of work and the human experience. Auerswald provides a timely investigation of value creation in the contemporary economy-and an indispensable guide to our economic future.

Homeschooling Corner: Erdos, Fibonacci, and some Really Big Numbers

One of the nice things about homeschooling is that it is very forgiving of scheduling difficulties and emergencies. Everyone exhausted after a move or sickness? It’s fine to sleep in for a couple of days. Exercises can be moved around, schedules sped up or slowed down as needed.

This week we finished some great books (note: I always try to borrow books from the library before considering buying them. Most of these are fun, but not books you’d want to read over and over):

The Boy who Loved Math: The Improbable Life of Paul Erdos, by Deborah Heligman, was a surprise hit. I’ve read a bunch of children’s biographies and been consistently disappointed; the kids loved this one. Improbable, I know.

I suppose the moral of the story is that kids are likely to enjoy a biography if they identify with the subject. The story starts with Erdos as a rambunctious little boy who likes math but ends up homeschooled because he can’t stand regular school. My kids identified with this pretty strongly.

The illustrations are nice and each page contains some kind of hidden math, like a list of primes.

Professor Astro Cat’s Frontiers of Space, by Dominic Walliman. This is a lovely book appropriate for kids about 6-11, depending on attention span and reading level. We’ve been reading a few pages a week and recently reached the end.

Minecraft Math with Steve, by Steve Math. This book contains 30 Minecraft-themed math problems (with three sub-problems each, for 90 total.) They’re fairly simple multiplication, subtraction, division, and multiplication problems, probably appropriate for kids about second grade or third grade. A couple of sample problems:

Steve wants to collect 20+20 blocks of sand. how much is that total?

Steve ends up with 42 blocks of sand in his inventory. He decides that is too much so drops out 12 blocks. How many blocks remain?

A bed requires 3 wood plank and 3 wools. If Steve has 12 wood planks and 12 wools, how many beds can he build?

This is not a serious math book and I doubt it’s “Common Core Compliant” or whatever, but it’s cute and if your kids like Minecraft, they might enjoy it.

We are partway into Why Pi? by Johnny Ball. It’s an illustrated look at the history of mathematics with a ton of interesting material. Did you know the ancient Greeks used math to calculate the size of the Earth and distance between the Earth and the moon? And why are there 360 degrees in a circle? This one I’m probably going to buy.

Really Big Numbers, by Richard Evan Schwartz. Previous books on “big numbers” contained, unfortunately, not enough big numbers, maxing out around a million. A million might have seemed really good to kids of my generation, but to today’s children, reared on Numberphile videos about Googols and Graham’s number, a million is positively paltry. Really Big Numbers delivers with some really big numbers.

Let’s Estimate: A book about Estimating and Rounding Numbers, by David A. Adler. A cute, brightly illustrated introduction. I grabbed notebooks and pens and made up sample problems to help the kids explore and reinforce the concepts as we went.

How Big is Big? How Far is Far? by Jen Metcalf. This is like a coffee table book for 6 yr olds. The illustrations are very striking and it is full of fascinating information. The book focuses both on relative and absolute measurement. For example,  5’9″ person is tall compared to a cat, but short compared to a giraffe. The cat is large compared to a fly, and the giraffe is small compared to a T-rex. My kids were especially fascinated by the idea that clouds are actually extremely heavy.

Blockhead: The Life of Fibonacci, by Joseph D’Agnes. If your kids like Fibonacci numbers (or they enjoyed the biography of Erdos,) they might enjoy this book. It also takes a look at the culture of Medieval Pisa and the adoption of Arabic numerals (clunkily referred to in the text as “Hindu-Arabic numerals,” a phrase I am certain Fibonacci never used.) Fibonacci numbers are indeed found all over in nature, so if you have any sunflowers or pine cones on hand that you can use to demonstrate Fibonacci spirals, they’d be a great addition to the lesson. Otherwise, you can practice drawing boxes with spirals in them or Pascal’s triangles. (This book has more kid-friendly math in it than Erdos’s)

Pythagoras and the Ratios, by Julie Ellis. Pythagoras and his cousins need to cut their panpipes and weight the strings on their lyres in certain ratios to make them produce pleasant sounds. It’s a fun little lesson about ratios, and if you can combine it with actual pipes the kids can cut or recorders they could measure, glasses with different amounts of water in them or even strings with rock hanging from them, that would probably be even better.

Older than Dirt: A Wild but True History of Earth, by Don Brown. I was disappointed with this book. It is primarily an overview of Earth’s history before the dinosaurs, which was interesting, but the emphasis on mass extinctions and volcanoes (eg, Pompeii) dampened the mood. I ended up leaving out the last few pages (“Book’s over. Bedtime!”) to avoid the part about the sun swallowing up the earth and all life dying at the end of our planet’s existence, which is fine for older readers but not for my kids.

Hope you received some great games and books last month!

The most racist post on this blog

Jesus loves the little children
All the little children of the world
Red and yellow, black and white
All are precious in his sight
Jesus loves the little children of the world

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From a review of Tomie dePaola’s Legend of the Indian Paintbrush:

The story is improperly sourced. Stories are a means to teach lessons for survival. Since this is a European perspective of a fantasy romanticized Indian of the past, this becomes another instance of whites with long lost culture dressing up and playing Indian . We need to know what tribe this story originates, the true setting and purpose of the original story, and the intended audience. The retelling doesn’t reflect the setting, material artifacts or even the specific nation it attempts to depict. The story and illustrations improperly depict native people as a mono-culture. The book makes native dialogue overly mystic. The use of words like “brave” “and papoose” instead of “man” and “child” dehumanize an entire group of people. Reading this to children will definitely perpetuate damaging stereotypes of the distinct cultures still alive and well today.

 

Review: Pete’s Dragon (the jr. novel) 2/5 stars

Warning: this post contains spoilers. Lots of spoilers.

I needed a break from politics, so I decided to read a book about a kid and his dragon. What better than the junior novelization of a Disney Movie?

Pete’s Dragon is a remake of the 1970s Disney live-action/animation mix of the same name. I saw the preview before finding Dory, thought it looked awesome, and so picked up the book-version. I assume that the book follows the movie’s plot accurately, though I do not expect it to capture the full cinematic experience.

Within the first chapter, that “awesome” feeling had diminished and I had the sinking feeling that the story was going to end with a generic, “kid goes to live among humans again” ending. And it did.

But let’s run through from the beginning:

5 year old Pete is on adventure with his parents in the woods when his dad crashes the car into a tree. His parents die. Pete emerges from the wreck, gets chased by wolves, and is saved by a big, shaggy dragon.

6 years later, Pete and the dragon (named Elliot) are best friends and run around the woods having fun, in a scene that I assume is spectacular in the movie.

The story then switches to the perspective of a bunch of grownups, each with their own plot and character arc. Mr. Meacham (IIRC) is an old guy who tells dragon stories to the local kids. His (grown) daughter, a forest ranger, doesn’t believe him. The forest ranger’s boyfriend has a brother who is a bumbling, vaguely evil logger who is illegally logging trees in the forest. The forest ranger, instead of arresting him for illegal logging, (which is what I assume happens when a forest ranger catches you illegally chopping down trees,) complains to her boyfriend that he’s not stopping his brother. He fails to stop his brother because he’s also useless.

To this cast of 6 (Pete, Elliot, Meacham, Forest Ranger, Boyfriend, and Brother,) we now add another eleven-year-old kid, Natalie. The Boyfriend is Natalie’s dad and he has for some reason brought her to the logging site in the woods, where she wanders around unsupervised while people chop down trees because that isn’t dangerous or anything.

Natalie notices Pete and the grown-ups catch him. Pete wakes up in the hospital, freaks out, and escapes in what I assume is another fun sequence in the movie. The grown-ups recapture him and the forest ranger and her boyfriend take him home, where they tame him with PBJ sandwiches and cookies.

Pete draws pictures of Elliot and promises to take his new friends to meet Elliot in the morning.

Meanwhile, Elliot has been looking everywhere for Pete. He follows Pete’s scent to the house where he’s staying, looks in the window, and decides that Pete has found a new family and doesn’t need him anymore. Elliot goes home.

Meanwhile, Forest Ranger lady is conflicted because she promised Pete she’d take him back to the woods to Elliot, who might be a dragon and might prove that her dad was right all along, but legally she’s required to take him to Child Protective Services. Finally she decides to take him to Elliot.

The Bumbling Brother shows up and shoots the dragon. With a gun. (With tranquilizer darts.) While Forest Ranger lady, Pete, Natalie, and Mr. Meacham are standing next to it. After the dragon collapses, the brother dances around proclaiming his success and no one punches him in the face, even though he could have killed them all (tranquilizers darts intended to bring down massive animals are potentially really bad if they hit children.)

The Bumbling Brother abducts the unconscious dragon, the grownups are useless, and Pete and Natalie (and Mr. Meacham) save the dragon. There’s a dramatic chase scene, at the end of which the Brother redeems himself by saving the Boyfriend and Forest Ranger’s lives.

Pete and the dragon escape back to the woods, where Pete suddenly decides that he doesn’t want to live with a dragon anymore and returns to the Forest Ranger’s house. You know, the lady who would have turned him over to CPS so he could go live in foster care without blinking an eye if he hadn’t claimed to have been living with a dragon.

In the final chapter, Pete and his new family (Forest Ranger, Boyfriend, and Natalie) drive to the mountains, where they visit the dragon, who (after Pete abandoned him) wandered off and randomly found his family of dragons.

So what’s wrong with this story?

For starters, it suffers from Too Many Characters. This is a kid’s book (movie.) Kids are interested in the antics of other kids; kids aren’t interested in adults trying to manage their adult relationships. With so many adult characters working through their own issues and character arcs, there is very little room in the story (it’s a short book) for Pete to have an arc of his own. In fact, Pete does not have a character arc. He does not debate whether or not he should join the humans, just spontaneously decides it for no particular reason at the end of the story.

Look, dragons are awesome. Living with a dragon is awesome. Most kids also think their own parents are pretty awesome. Random grownups you don’t know are not awesome. A life of wearing clothes, going to school, and doing homework is way less awesome than living in the woods with your dragon. Pete abandoning his dragon makes as much sense as a child spontaneously abandoning a beloved pet.

In short, the ending is completely unmotivated and makes no sense.

It still might be a fun movie (the special effects looked nice in the preview,) so long as the “gun-toting, ginger ale-swigging, bumbling logger” as bad guy doesn’t annoy you too much. But I was genuinely disappointed by the book.