Do go see the Merchants of Doubt Movie. Los Angeles and New York March 6 opening, Chicago, San Francisco, and Washington DC the 13th. More widely starting the 20th of March. The movie is inspired by the book of the same name, about how it is established industries can sell Doubt even in areas where the science is pretty well established.
The movie is not the book, nor does it make the mistake of trying to put the book on screen. But it does pick up many of the threads, and, most importantly, shows well how the Merchants of Doubt ply their trade. And it does so in an engaging way. One element of that being the extended visual, and practical, illustration of close up magic. Sleight of hand, misdirection, using shills (3 card monte was the example for this) all have their analogues for the Merchants of Doubt.
The phrase itself derives from the tobacco industry, PR firm, which concluded that doubt was their product -- they could not argue the science, but they could still cast doubt. Decades later (not from the movie) this was echoed by Frank Luntz, who also observed "A compelling story, even if factually inaccurate, can be more
emotionally compelling than a dry recitation of the truth," Mr Luntz
notes in the memo.
We see this item primarily through the flame retardants theme in the move. A doctor testifies to legislators about the harrowing death of a child, burned on the parts of its body that were on the non-flame-retardant pillow as opposed to the flame-retarded mattress. Once the testimony is given, the bill to lift requirements for the chemicals is promptly defeated. Except, it turns out, and the doctor confirms, that the events in his testimony never actually happened. But his story was far more compelling that mere recitation of facts about the (in)effectiveness of the fire-retardants. And that's the important part. (? For the doctor, at least, and his funders. See who that turns out to be.)
How did we get to fire retardants from tobacco? Cancer is a long stretch from fire, after all. But that's part of the tangled web of merchandising Doubt. Burning cigarettes start fires. Tobacco companies could have been told to develop cigarettes that didn't burn so long unattended. Rather than do so (potentially expensive), they pushed the argument, successfully, that the problem was the couches/mattresses/pillows. They shouldn't catch fire so easily; that was the real problem. If you can convince people that it's the fault of couches for letting themselves be burned, rather than of the cigarettes for burning couches (thence homes and people), there are few limits to what you can convince people of.
That's one of the methods of the PR flacks, and those methods are what the movie explores in a number of difference stories and ways. Climate looms large in the movie, larger than in the book. That renders it a little hard for me to say much about -- I have too much first hand experience with the people and events. What I can say from that first hand knowledge (or at worst second hand) is that it represents well how the people in the climate 'debate' actually talk. And I can say with some confidence that it represents them fairly. That's true whether it's Marc Morano (who's quite up front about the fact that he is attacking the scientists, not the science, and is pleased about the hate-mail that scientists get after he releases their email addresses) or Katharine Hayhoe (receiving end of some of that hate-mail, a scientist working on understanding climate who has been talking publicly to groups about creation care). Katharine is also a conservative evangelical Christian. One of the themes in the moving being about tribalism, so such identifiers sometimes are important.
I don't give away much, the meat is how you get to this point, in observing that I also like Producer/Director Robert Kenner's choice to end the movie with some optimism from Bob Inglis (6 time congressman elected from very conservative part of very conservative South Carolina) as to his belief that the problems of climate change are real (which got him massacred in his last primary) and can be addressed. The Merchants of Doubt have their successes, as does the magician. But, as more people see how the trick is done, the fewer who fall for it. I hope. See the movie and let me know in the comments what you think.
Since I was at a special preview, I'll write a separate note about that, and about some of the discussion we had with Kenner after the movie.
In the mean time, some potentially useful other links:
Movie's official web site with release dates
Rotten Tomatoes
IMDb
Showing posts with label history. Show all posts
Showing posts with label history. Show all posts
05 March 2015
04 October 2011
Climate change science history
A question at the question place regarded this history of climate change science. See that link for the full question. Here's my response, which over-ran the blog comment length limit there.
On the historical link side, the best single source is Spencer Weart's _The Discovery of Global Warming_ http://www.aip.org/history/climate/index.htm I think he under-rates the significance of G. S. Callendar's work in the 1930s-50s, but that's my take and I haven't yet written it up formally. It's an idea I've had, to do so. Unfortunately, a lot of Weart's work will probably pass your students' level. But you should be fine with it yourself and translate suitably to your students.
A different matter for your middle schoolers is the time scale of the history. Perhaps combine this with a project to collect family history?
I'll take your students as being about 11. Let's go with 30 years per generation, which is, at least, fairly accurate for my family. That means your kids born in about 2000, parents about 1970, grandparents in 1940, great-grandparents in 1910, great-great grandparents in 1880, great-great-great grandparents (g3 grandparents) in 1850, and g4 grandparents in 1820.
The history is:
On the historical link side, the best single source is Spencer Weart's _The Discovery of Global Warming_ http://www.aip.org/history/climate/index.htm I think he under-rates the significance of G. S. Callendar's work in the 1930s-50s, but that's my take and I haven't yet written it up formally. It's an idea I've had, to do so. Unfortunately, a lot of Weart's work will probably pass your students' level. But you should be fine with it yourself and translate suitably to your students.
A different matter for your middle schoolers is the time scale of the history. Perhaps combine this with a project to collect family history?
I'll take your students as being about 11. Let's go with 30 years per generation, which is, at least, fairly accurate for my family. That means your kids born in about 2000, parents about 1970, grandparents in 1940, great-grandparents in 1910, great-great grandparents in 1880, great-great-great grandparents (g3 grandparents) in 1850, and g4 grandparents in 1820.
The history is:
26 January 2011
Rabett on History of Radiation
No surprise to you that I'm interested in the history of science and of knowledge, but perhaps a little surprising that I'm not the only one. Eli Rabett has recently taken up the history of our knowledge on atmospheric infrared radiation.
http://rabett.blogspot.com/2011/01/required-reading.html Ångström observing infrared radiation from the atmosphere
http://rabett.blogspot.com/2011/01/angstrom-effect.html Arguing for 'Ångström effect' as the name rather than greenhouse effect
but then joining many of the rest of us in 'Callendar effect' in http://rabett.blogspot.com/2011/01/well-damn-it-all-its-callendar-effect.html
http://rabett.blogspot.com/2011/01/fourier-and-greenhouse.html More about Fourier and the term 'greenhouse effect'
I'll also take this chance to recommend The Callendar Effect as being a readable introduction both to the biography of the engineer/scientist who did the work, and to the science that he did on carbon dioxide as an important driver of climate change. I also have the complete papers, one of which and its response have some interesting, to me at least, illumination regarding the difference between being skeptical and being in denial.
http://rabett.blogspot.com/2011/01/required-reading.html Ångström observing infrared radiation from the atmosphere
http://rabett.blogspot.com/2011/01/angstrom-effect.html Arguing for 'Ångström effect' as the name rather than greenhouse effect
but then joining many of the rest of us in 'Callendar effect' in http://rabett.blogspot.com/2011/01/well-damn-it-all-its-callendar-effect.html
http://rabett.blogspot.com/2011/01/fourier-and-greenhouse.html More about Fourier and the term 'greenhouse effect'
I'll also take this chance to recommend The Callendar Effect as being a readable introduction both to the biography of the engineer/scientist who did the work, and to the science that he did on carbon dioxide as an important driver of climate change. I also have the complete papers, one of which and its response have some interesting, to me at least, illumination regarding the difference between being skeptical and being in denial.
28 November 2009
Science Anniversaries
150 and 400 years ago, two major events in the history of science occurred.
400 years ago, the telescope was invented and started to be used for astronomy. For $100-$150 you can now get a telescope far superior to what Galileo used to carry out a major revolution in our understanding of the universe. More in a moment.
150 years ago yesterday (November 27th), Charles Darwin's On the Origin of Species by Means of Natural Selection was published. Different major revolution in our understanding of the universe. You can read this for yourself. I don't actually recommend reading it unless you are really interested in history of science, and like Victorian-era writing. (If you like my style, you're a couple steps in that direction. My wife noted that I write something like Trollope, a prolific Victorian whom she likes.) We've learned an awful lot in the 150 years since then, and many things that were mysteries to Darwin, such as how inheritance occurs, are well-known to us now. Instead I'll suggest you read the evolution sections of modern biology texts. Two such texts recommended by my biologist friends are Futuyma's, and Campbell and Reece.
400 years ago, the telescope was invented and started to be used for astronomy. For $100-$150 you can now get a telescope far superior to what Galileo used to carry out a major revolution in our understanding of the universe. More in a moment.
150 years ago yesterday (November 27th), Charles Darwin's On the Origin of Species by Means of Natural Selection was published. Different major revolution in our understanding of the universe. You can read this for yourself. I don't actually recommend reading it unless you are really interested in history of science, and like Victorian-era writing. (If you like my style, you're a couple steps in that direction. My wife noted that I write something like Trollope, a prolific Victorian whom she likes.) We've learned an awful lot in the 150 years since then, and many things that were mysteries to Darwin, such as how inheritance occurs, are well-known to us now. Instead I'll suggest you read the evolution sections of modern biology texts. Two such texts recommended by my biologist friends are Futuyma's, and Campbell and Reece.
21 July 2009
Timelines
Some ages ago, I assembled time lines. Seeing JG's much better timelines (and with live content, vs. my static listing) reminded me of them. One feature of my approach was to use a set of time lines, each of which was about 10 times shorter than the previous. That number varied widely in practice. But the basic idea was to take a look over time to the present, focusing more and more narrowly towards the present.
I did this so long ago that a number of dates need to be changed -- the universe is 13.7 billion years old, not the 15 that I used, for instance. Still, here's a look at the version of my timelines from 10 years ago, from shortest to longest periods. Maybe I can entice JG to using this idea for his version?
In any case, enjoy. When you see things that are dated wrong, or see important things that should be added, do comment. (There's no question of if; even my casual glances were showing a lot of things in need of update and addition or deletion.)
I did this so long ago that a number of dates need to be changed -- the universe is 13.7 billion years old, not the 15 that I used, for instance. Still, here's a look at the version of my timelines from 10 years ago, from shortest to longest periods. Maybe I can entice JG to using this idea for his version?
In any case, enjoy. When you see things that are dated wrong, or see important things that should be added, do comment. (There's no question of if; even my casual glances were showing a lot of things in need of update and addition or deletion.)
18 February 2009
First successful numerical weather prediction
Wasn't planning on such a gap between posts, but life happens. In the previous note, I talked a little about the first numerical weather prediction. One of the comments (you do read the comments, I hope, lots of good material there!) mentioned an article by Peter Lynch on the first successful numerical weather prediction.
I was of two minds about that article. On the plus side, it is a well-written article about something I'm interested in. On the minus side, I was planning on writing an article much like it myself and now I can't :-( On the plus side, I got to read the article without doing all the work I'd have had to do in writing it myself. In any case, go read it!
The first successful numerical weather prediction was done using the ENIAC computer, and took almost 24 hours to make a 24 hour prediction. I learned numerical weather prediction from George Platzman, who was involved in that effort. When he learned of my interest in history, he gave me copies of his notes from his 1979 lecture, in which he'd re-created the original model. This included, for instance, a lot of work being done to scale all numbers to be a magnitude near 1. The ENIAC did fixed-point arithmetic. If the numbers weren't close to 1, the precision would have rapidly decayed.
One thing I did after programming the model was let it run out farther in to the future than the original 24 hours. The model blew up. A quick check with Platzman showed that this was no surprise. He had (40 years earlier) done the numerical analysis on the boundary conditions and showed that a) they were unstable but b) the instability was 'slow' -- would not affect the 24 hour results. My result confirmed that, though he was surprised that it started to blow up around day 3 and was useless by day 5.
The more refined test, on a model that included the same physical processes but did not include the boundary condition problem, was done by P. D. Thompson, "Uncertainty of initial state as a factor in the predictability of large scale atmospheric flow patterns", Tellus, 9, p275-295, 1957. It appears that he was the first to publish on whether there were intrinsic limits to how far ahead one could predict the weather. Up to about this time, the sentiment had been that if given enough computing power (something much larger than the WPA project Eli mentioned in the previous comments was envisioned by Richardson) and good enough data, then working out the data management (source of Richardson's problems) and numerical representation would suffice to get meaningful answers about weather indefinitely far into the future.
Thompson conducted an experiment on that presumption. Suppose that we start a weather forecast model from two slightly different initial conditions. So slightly different, in fact, that no plausible observing system would be able to tell the difference between them. Would the two forecasts also start and remain too close together for observations to tell the difference? The surprising (at that time) answer was, no. These unobservably small differences would lead to easily observed differences once you were far enough in to the forecast. Worse, 'far enough', wasn't terribly far -- only a week or two.
In 1963, while working on a highly simplified model of convection, Ed Lorenz bought dynamical chaos in to meteorology. Some of his work is described at non-professional level in James Gleick's Chaos: Making a New Science, as are some of the implications. This book was written in 1988, at the peak of the optimism about chaos. Things got messier later. But the history isn't too bad and the descriptions of chaos are helpful.
The key for the moment is that this business of initial states that are quite close to each other giving predictions that are quite different is one of the symptoms of chaos. They're also a symptom of a bad model or programming error, so you have to do some work, as Thompson and Lorenz did, to show that you're dealing with chaos rather than errors.
I started in the previous post with some comments about chaos and climate. We're about ready for those. A couple things to remember are that numerical weather prediction goes back a long way, and that from very early on there have been questions about just how far ahead we could predict weather. What is still open is what any of this -- chaos, limited predictability of weather, difficulty of writing error-free models -- means for climate. More next time.
I was of two minds about that article. On the plus side, it is a well-written article about something I'm interested in. On the minus side, I was planning on writing an article much like it myself and now I can't :-( On the plus side, I got to read the article without doing all the work I'd have had to do in writing it myself. In any case, go read it!
The first successful numerical weather prediction was done using the ENIAC computer, and took almost 24 hours to make a 24 hour prediction. I learned numerical weather prediction from George Platzman, who was involved in that effort. When he learned of my interest in history, he gave me copies of his notes from his 1979 lecture, in which he'd re-created the original model. This included, for instance, a lot of work being done to scale all numbers to be a magnitude near 1. The ENIAC did fixed-point arithmetic. If the numbers weren't close to 1, the precision would have rapidly decayed.
One thing I did after programming the model was let it run out farther in to the future than the original 24 hours. The model blew up. A quick check with Platzman showed that this was no surprise. He had (40 years earlier) done the numerical analysis on the boundary conditions and showed that a) they were unstable but b) the instability was 'slow' -- would not affect the 24 hour results. My result confirmed that, though he was surprised that it started to blow up around day 3 and was useless by day 5.
The more refined test, on a model that included the same physical processes but did not include the boundary condition problem, was done by P. D. Thompson, "Uncertainty of initial state as a factor in the predictability of large scale atmospheric flow patterns", Tellus, 9, p275-295, 1957. It appears that he was the first to publish on whether there were intrinsic limits to how far ahead one could predict the weather. Up to about this time, the sentiment had been that if given enough computing power (something much larger than the WPA project Eli mentioned in the previous comments was envisioned by Richardson) and good enough data, then working out the data management (source of Richardson's problems) and numerical representation would suffice to get meaningful answers about weather indefinitely far into the future.
Thompson conducted an experiment on that presumption. Suppose that we start a weather forecast model from two slightly different initial conditions. So slightly different, in fact, that no plausible observing system would be able to tell the difference between them. Would the two forecasts also start and remain too close together for observations to tell the difference? The surprising (at that time) answer was, no. These unobservably small differences would lead to easily observed differences once you were far enough in to the forecast. Worse, 'far enough', wasn't terribly far -- only a week or two.
In 1963, while working on a highly simplified model of convection, Ed Lorenz bought dynamical chaos in to meteorology. Some of his work is described at non-professional level in James Gleick's Chaos: Making a New Science, as are some of the implications. This book was written in 1988, at the peak of the optimism about chaos. Things got messier later. But the history isn't too bad and the descriptions of chaos are helpful.
The key for the moment is that this business of initial states that are quite close to each other giving predictions that are quite different is one of the symptoms of chaos. They're also a symptom of a bad model or programming error, so you have to do some work, as Thompson and Lorenz did, to show that you're dealing with chaos rather than errors.
I started in the previous post with some comments about chaos and climate. We're about ready for those. A couple things to remember are that numerical weather prediction goes back a long way, and that from very early on there have been questions about just how far ahead we could predict weather. What is still open is what any of this -- chaos, limited predictability of weather, difficulty of writing error-free models -- means for climate. More next time.
26 January 2009
Happy New Year
Happy Chinese New Year!
(a little late, and we're still in question whether it's year of the Cow or what)
(a little late, and we're still in question whether it's year of the Cow or what)
27 November 2008
Population density and climate
Something that struck me quite some time ago as an eyeball-quality correlation was that population density was higher in areas with more rainfall. A proper study of this should look through history and be rigorous about both quantities. As long distance large-scale trade of food became possible, it'll also be necessary to look at water requirement for food and for people separately.
But, as a start and something of a Fermi estimate, let's go with what my water company tells me is typical per-person water usage at home -- 70 gallons per day. That's approximately 250 liters. Typical annual rainfall around here is about 1 meter per year. So, if I captured all the rain that fell on an area, how large would that area have to be for me? If everyone else did the same, how many of us could live in 1 square km?
The 250 liters are what I'd use in 1 day if I were approximately 'average'. For a year, I need 365 times that much, for about 100,000 liters. 1 liter is a cube 0.1 m on a side, so for a year's water I need about 100 m^3. That'd fill 1 meter deep across a square 10 meters by 10 meters. Since I get about 1 meter rain per year, this says I need about 100 square meters. If we all caught absolutely all rain, and didn't lose any to trees, farming, industry, grass, ... this would let us go up to a population density of 10,000 per square km (about 25,000 per square mile). This is actually something like the density of the highest density large cities -- check out Chicago, New York, London, Paris, for instance.
Los Angeles gets about 0.4 m/year rain, and Phoenix about 0.2. Their densities are about 3200 and 1200, respectively (Wikipedia for both numbers) to the 4000 and 2000 we'd guess from the above. On the other hand, both illustrate the fact that cities don't rely on only the rain that falls on them. Both have extensive systems to bring water to them. This is not new; Rome, to support its population in the days of Empire, when they were also using 10s of gallons of water per day, built a tremendous system of aqueducts to bring water to the city. New York and Chicago, I know, also bring water from outside the city (again water systems, rivers, lakes).
If we figure on about 10% of the rainfall being captured for residential use, then we're down to about 1000 per square km, or about 2500 per square mile in my part of the country. These are densities comparable to what is seen over moderately large areas (entire metropolitan regions, small countries in northern and western Europe, ...) with meter per year rainfall.
There's a certain reasonability, then, to there being a relation between rainfall and population density. You can exceed that relation, but only at the expense of building a large scale water system -- and not letting people in the areas you bring water from have acess to it. Water rights have a lengthy and often not peaceful history. In any case, even if all the people are in one place, their requirement extends over a larger area, something more in accord with the 1000 per square km (for 1 m/year rainfall). For, say, the 20 million or so people in the Los Angeles area, with 0.4 m/year of rain, it says their footprint is more like 50,000 km^2, to the 4000 or so the metro area actually occupies. These are all still Fermi estimates, of course. It does point out to us, however, that urban areas likely have a footprint rather larger than the official area. Conversely, it means that they need to be concerned about weather and climate over a larger area than just their own borders.
The climate concern is ... what do you do if you get less rain (or less snow)? What do you do if the rain comes more in situations (thunderstorms) that are harder for you to capture the rainwater from? Either of these changes drives you to a lower population in your urban (including suburbs) area, or pins a new expense on you -- to build more extensive water systems, and systems able to handle greater rainfall rates. While we mostly expect rainfall to increase, we do expect that there will be areas which will see falls. Which ones, not so sure, but some. It's expected and observed that there'll be an increase in rain falling in heavy doses even where there's little change in total rainfall.
A friend mentioned a TV person-in-the-street interview during a local drought. The person wasn't concerned about the drought and low river levels because "I don't get my water from the river, I get it from the tap." I trust you all know that it went from the river through a processing system to her tap even if she didn't. (Or, in the case of Chicago, from Lake Michigan -- whose level seems to be falling more than the usual cycle.)
It seems more people don't realize that wells have the same problem. Across much of the Great Plains US (take Kansas for a central example state), farming and residences take advantage of the Ogallala Aquifer. The problem is, aquifers need recharging. That is, the water in the aquifer comes from rainfall elsewhere. With the Ogallala today, the usage exceeds the recharge rate. This is not very surprising, as the aquifer's recharge area is itself in fairly dry areas. For more, see the USGS web site, search on Ogallala Aquifer and recharge. Similar problems exist for many wells. The main difference between wells and lakes or rivers is that you can't see the levels dropping as easily.
But, as a start and something of a Fermi estimate, let's go with what my water company tells me is typical per-person water usage at home -- 70 gallons per day. That's approximately 250 liters. Typical annual rainfall around here is about 1 meter per year. So, if I captured all the rain that fell on an area, how large would that area have to be for me? If everyone else did the same, how many of us could live in 1 square km?
The 250 liters are what I'd use in 1 day if I were approximately 'average'. For a year, I need 365 times that much, for about 100,000 liters. 1 liter is a cube 0.1 m on a side, so for a year's water I need about 100 m^3. That'd fill 1 meter deep across a square 10 meters by 10 meters. Since I get about 1 meter rain per year, this says I need about 100 square meters. If we all caught absolutely all rain, and didn't lose any to trees, farming, industry, grass, ... this would let us go up to a population density of 10,000 per square km (about 25,000 per square mile). This is actually something like the density of the highest density large cities -- check out Chicago, New York, London, Paris, for instance.
Los Angeles gets about 0.4 m/year rain, and Phoenix about 0.2. Their densities are about 3200 and 1200, respectively (Wikipedia for both numbers) to the 4000 and 2000 we'd guess from the above. On the other hand, both illustrate the fact that cities don't rely on only the rain that falls on them. Both have extensive systems to bring water to them. This is not new; Rome, to support its population in the days of Empire, when they were also using 10s of gallons of water per day, built a tremendous system of aqueducts to bring water to the city. New York and Chicago, I know, also bring water from outside the city (again water systems, rivers, lakes).
If we figure on about 10% of the rainfall being captured for residential use, then we're down to about 1000 per square km, or about 2500 per square mile in my part of the country. These are densities comparable to what is seen over moderately large areas (entire metropolitan regions, small countries in northern and western Europe, ...) with meter per year rainfall.
There's a certain reasonability, then, to there being a relation between rainfall and population density. You can exceed that relation, but only at the expense of building a large scale water system -- and not letting people in the areas you bring water from have acess to it. Water rights have a lengthy and often not peaceful history. In any case, even if all the people are in one place, their requirement extends over a larger area, something more in accord with the 1000 per square km (for 1 m/year rainfall). For, say, the 20 million or so people in the Los Angeles area, with 0.4 m/year of rain, it says their footprint is more like 50,000 km^2, to the 4000 or so the metro area actually occupies. These are all still Fermi estimates, of course. It does point out to us, however, that urban areas likely have a footprint rather larger than the official area. Conversely, it means that they need to be concerned about weather and climate over a larger area than just their own borders.
The climate concern is ... what do you do if you get less rain (or less snow)? What do you do if the rain comes more in situations (thunderstorms) that are harder for you to capture the rainwater from? Either of these changes drives you to a lower population in your urban (including suburbs) area, or pins a new expense on you -- to build more extensive water systems, and systems able to handle greater rainfall rates. While we mostly expect rainfall to increase, we do expect that there will be areas which will see falls. Which ones, not so sure, but some. It's expected and observed that there'll be an increase in rain falling in heavy doses even where there's little change in total rainfall.
A friend mentioned a TV person-in-the-street interview during a local drought. The person wasn't concerned about the drought and low river levels because "I don't get my water from the river, I get it from the tap." I trust you all know that it went from the river through a processing system to her tap even if she didn't. (Or, in the case of Chicago, from Lake Michigan -- whose level seems to be falling more than the usual cycle.)
It seems more people don't realize that wells have the same problem. Across much of the Great Plains US (take Kansas for a central example state), farming and residences take advantage of the Ogallala Aquifer. The problem is, aquifers need recharging. That is, the water in the aquifer comes from rainfall elsewhere. With the Ogallala today, the usage exceeds the recharge rate. This is not very surprising, as the aquifer's recharge area is itself in fairly dry areas. For more, see the USGS web site, search on Ogallala Aquifer and recharge. Similar problems exist for many wells. The main difference between wells and lakes or rivers is that you can't see the levels dropping as easily.
19 November 2008
Large population countries
Partly because of my recent trip(s) to China, but also as a continuation of an idea I started in describing the ocean and atmospheres, I'll take a look at national populations today. In terms of the oceans and atmospheres, I looked at what they were composed of, and found that rather few elements were sufficient to cover a large fraction of each. It's a larger group for countries, but, still, fairly few (less than 10%) of the countries are required to cover over half the population of the world. The figures here are 2005 numbers from the 2006 Information Please almanac. They'll have changed since then, of course, but more on that later.
From a world population of about 6.6 billion, countries with more than 1% of the world's population are:
One thing this suggests to me is that for modern citizenship, history, geography classes, it would be a good idea to learn some specifically about these countries. If only some bare elements of things like capitals, languages, religions, a bit of history, etc. In bygone days (i.e., when I was in elementary school), we did do that sort of thing, but only for the US plus western Europe. You'll notice up there that only 1 western European country is on the list. In other words, such an education didn't do much good towards living in the world I find myself in. Actually, my school did cover most of the rest of the list but we were distinctly odd for our time and area.
The up side of learning about this set is that, while it covers a large fraction of the world's people, the list is short.
If I drew up the comparable list for, say, 1939, you'd find far more European countries present. If I did it for 1880 or so, it would be even more Europe-heavy. I'll get to those at a later date.
A different list would show up if I did it in terms of the global economy, one much heavier on Europe. But you'd see many of the same contries on that list. 4 of the G8 are already on this listing. And a somewhat different list would show up by listing countries in terms of land area. Again, though, most of the largest are already given (in rough order, the biggest are Russia -- huge, Canada, US, China, Brazil, Australia -- all very large, India, ... 5 of the 7 are already shown above).
If anyone would like to take on constructing comparable lists -- countries with 1% or more of world GDP, countries with 1% or more of world land area, countries with 1% or more of world ocean EEZ area (Indonesia moves way up!) inside their exclusive economic zones (EEZ) -- please do send it in. I'll get there one of these days, but, as my recent posting rate suggests, this can be a while.
The European Union presents some problems for such list-making. The current EU-27 represents about 500 million people, so would be number 3 on the above list (and take Germany off it), and be a 'country' larger than India in land area. On the other hand it isn't exactly a country. I just finished reading Postwar: A history of Europe from 1945 to the Present by Tony Judt. 'What is Europe' is a more interesting question than I'd thought.
From a world population of about 6.6 billion, countries with more than 1% of the world's population are:
- China, 1300 million
- India, 1100
- USA 295
- Indonesia 242
- Brazil 186
- Pakistan 162
- Bangladesh 144
- Russia 143
- Nigeria 128
- Japan 127
- Mexico 106
- Philipines 88
- Vietnam 84
- Germany 82
- Egypt 77.5
- Ethiopia 73.1
- Turkey 69.7
- Iran 68.0
One thing this suggests to me is that for modern citizenship, history, geography classes, it would be a good idea to learn some specifically about these countries. If only some bare elements of things like capitals, languages, religions, a bit of history, etc. In bygone days (i.e., when I was in elementary school), we did do that sort of thing, but only for the US plus western Europe. You'll notice up there that only 1 western European country is on the list. In other words, such an education didn't do much good towards living in the world I find myself in. Actually, my school did cover most of the rest of the list but we were distinctly odd for our time and area.
The up side of learning about this set is that, while it covers a large fraction of the world's people, the list is short.
If I drew up the comparable list for, say, 1939, you'd find far more European countries present. If I did it for 1880 or so, it would be even more Europe-heavy. I'll get to those at a later date.
A different list would show up if I did it in terms of the global economy, one much heavier on Europe. But you'd see many of the same contries on that list. 4 of the G8 are already on this listing. And a somewhat different list would show up by listing countries in terms of land area. Again, though, most of the largest are already given (in rough order, the biggest are Russia -- huge, Canada, US, China, Brazil, Australia -- all very large, India, ... 5 of the 7 are already shown above).
If anyone would like to take on constructing comparable lists -- countries with 1% or more of world GDP, countries with 1% or more of world land area, countries with 1% or more of world ocean EEZ area (Indonesia moves way up!) inside their exclusive economic zones (EEZ) -- please do send it in. I'll get there one of these days, but, as my recent posting rate suggests, this can be a while.
The European Union presents some problems for such list-making. The current EU-27 represents about 500 million people, so would be number 3 on the above list (and take Germany off it), and be a 'country' larger than India in land area. On the other hand it isn't exactly a country. I just finished reading Postwar: A history of Europe from 1945 to the Present by Tony Judt. 'What is Europe' is a more interesting question than I'd thought.
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