The Battle of New Orleans, one of the major battles of the War of 1812, was fought on January 8th, 1815. The War of 1812 had ended the previous December. Awkward. In South Africa, the “Second Boer War” broke out for a number of reasons, but the common thread was about how the various territories of the region should be organized and governed. War was declared in October 1899, and formally ended on May 31st, 1902. The political and ideological struggle continued, and it was not until 1910 that the first official agreement to address the initial reasons for the war emerged. But even after that the struggle continued. The American Civil War ended on April 9th, 1865. A half dozen major battles and 16 months later, the fighting in that ended war petered out. The ideological struggle related to that war continues today, and thousands have died over it, after it was over.
A purely ideological war (though not without material casualties) is the war against the teaching of evolution in American public schools. There was a lot of action in that war throughout much of the 20th century. On December 20th, 2005, the United States District Court for the Middle District of Pennsylvania decided Tammy Kitzmiller vs. Dover Area School District in favor of the science of evolution being taught unfettered, and identified the last breath of a pseudo-scientific creationist doctrine as an expression of religion. Sure, people still continued to fight over the issue, but after the Dover decision, there were very few significant fights in public schools over evolution, the battles being brought to state legislatures, where they never took root because of Dover. Fighting continued, ideological battles continued, just like in all those other wars, but the war on evolution in the US public school system ended in December 2005.
I declare the war on science over this month, July, 2020. Nice round patriotic number. We can pick a date later after history has sorted out some details. But the war ended when this happened: American anti science forces having spent months telling people that Covid-19 was a hoax, not really deadly, not really as bad as it seemed, and that masks did not really matter … well, they started wearing masks. Pence and Trump surrendered the war when they said wear masks. The people in my local grocery store, that had been not wearing masks, masked up. The end. War over.
Most of my friends are pedantic skeptics, just like you dear reader, and you won’t let me say that the war on science is over because bla bla bla bla. That is why I wrote the little introduction at the beginning of this blog post. If we treat every thing like we were Wikipedia editors, than every thing would be slightly to very warped and things like wars would never be over. Get over it. This war is over, even if sporadic fighting continues until the Sun expands.
By the way, did you notice that there are some wars that actually unambiguously end, like World War II? Do you know why they get to end but other wars, from a pedantic perspective, never do? I’m not sure but I think those are wars started by individuals, or small groups of different kings or leaders, then when the opposition (usually, the good guys) catch up to them and put them down, the war ends, more or less instantly. But I digress.
There is still a fight, there are still more fights over science and justice and all that. But the systematic Republican controlled war on science in America got won. By us.
If you read a lot of books about cosmology and the universe, you will not find much new in this book, but you will find newways to think about all that old stuff. If you really do have a newtheory of everything, this book will give you some useful advice on how to buy your ticket into the physics game. Like, that you have to make sure your theory of everything works in a way that does not result in the night sky being as bright as the day sky, or makes light do something it does not do, and so on. Also, do not use many different TYPEFACES AND all caps in your write-up.
Interestingly, one of the things the actual-cosmologists-authors do NOT say is something I often hear from pro-physicists about TOE-pushers. They don’t say “if you don’t have a mathematical formula for your theory, it isn’t a theory.” I hear that all the time and I always thought there was something wrong with that. Seems to me that a totally wrong mathematical theory is too much of a likelihood.
The best overview of this book, which you SHOULD read, is from the authors themselves who made a video talking about the book. Here:
… will be written in about three years from now. Meanwhile …
We labor under a number of falsehoods about how science works. Even scientists do. There are considerable differences among the panoply of scientific disciplines, and these are important enough that I would never trust the practitioners of one scientific discipline to, say, review research procedures or grant proposals from another discipline, by default.
These differences are even more significant outside of science itself. A common example is this one. A lay person evaluating peer reviewed research claims that a certain scientific conclusion can not be supported because there have been no double blind studies. That person may be unaware of the fact that almost no science uses double blind studies. This is a methodology used only in some areas of research. A study of earthquake hazards, genetic phylogeny of chickadees, or how long a particular virus lingers on a surface will not have a double blind methodology.
In some fields of study, a single idea will often be represented by a single major publication (sometimes a book) and will not be seen elsewhere unless it is being criticized. This is not common in the true sciences, per se, but this does happen in the peer reviewed literature. In other fields of study, a single idea may be addressed in hundreds of peer reviewed papers. In some fields of study, if a published peer reviewed paper presents a conclusion that is thought to be wrong, because of some flaw, the scholars in that field are expected to learn of this problem and thereafter avoid citing that paper. In other fields, when this happens, the paper is withdrawn from the literature after the invocation of complex rituals that might or might not involve the sounding of trumpets.
There seems to be two falsehoods affecting some of our thinking about COVID-19. One is the idea that a “study” or “publication” about some detail of the disease tells us something that we can take as fact. Yes, Covid-19 stays on a certain kind of surface for N days, therefore we can’t do X! That sort of thing. However, this research is, firstly, not peer reviewed. There may very well be no peer reviewed papers on COVID-19 at this time. This Pandemic has lasted less time that the typical peer review process takes. Maybe there are a few out there, but mostly, we are dealing with non-reviewed work, or work in review. This is good work, and important work, but it is more like a set of “emergency results” that address specific pressing questions in a provisional way.
It has been important to decide which of a small number of broad categories COVID-19 can be placed in, and the work on persistence on various surfaces has provided that rough and ready guide. There are pathogens that can find their way out of an exam room, go 20 feet down the hall, and infect a person sitting in a different exam room. There are pathogens that are so unlikely to infect another person that you practically have to lick the inside of their mouth five times to catch the disease. COVID-19 is in the in between category, where it sheds into the air and hangs around on surfaces for long enough that surfaces are found to have the virus on them. Is COVID-19 more or less surface-contaminating than, say, norovirus? Rotavirus? Nobody knows, because the research to determine that, and the publication array that would be necessary to lead to policy and recommendations about that, will take time. Someday there will be a study that looks at how much of the virus persists for how long on various surfaces, integrated with the other important question of how can the virus on a given surface actually infect a human, in order to allow for a realistic and useful statement about how to go about keeping a home, and ICU, an examining room, or a school relatively safe. COVID-19 has the potential to be the most studied pathogen in recent history, but not today.
So, that is the first fallacy: that a handful of quick and dirty, rough and ready, studies designed to get a clue about this disease constitute a well tempered and developed peer reviewed literature from which we can glean an accurate characterization of most o fhte important details of this disease. Nope.
One cost of this fallacy is the second fallacy, that we can evaluate models of either COVID-19’s behavior, or the efficacy of our reaction to it, based on a solid knowledge of the disease. That is backwards. We will eventually be able to evaluate ideas like “curve flattening” by understanding a lot about COVID-19, but that will happen after we have actually seen what various curve flattening efforts have done. A recent proposal that certain areas of the world may have seen a prior passage of COVID-19, causing some local immunity. One well meaning expert (not an actual expert) on social media responded that given the way COVID-19 operates, this is simply impossible. But that is backwards. The way we will eventually be able to describe how COVID-19 actually works is by observing it, measuring it, developing good explanations for what we see, strengthening and tempering those explanations by further hypothesis testing, replication, critique in the formal peer review process as well as the less formal but sometimes more important conversations at the conference-bar setting, and time. Time to just think. Then, we will be able to say things like “X is pretty much impossible because this is how COVID-19 works.” Now, we have an expansive void where some good theory and data will eventually reside, and the job of the scientists focused on this problem is to carefully and thoughtfully fill that void with what they come to know. To get a sense of how this works, read up on the literature that came out of the 2013 Ebola epidemic. Many key known things about the pathogen and its effects were not nailed down until months or years after the last patient was identified. These things take time.
The BBVA Foundation has awarded climate scientist Kerry Emanuel the Frontiers of Knowledge Award in Climate Change.
MIT’s press release:
Emanuel’s research has provided fundamental contributions to understanding of tropical cyclones and how they are affected by climate change.
The BBVA Foundation — which promotes knowledge based on research and artistic and cultural creation, and supports activity on the analysis of emerging issues in five strategic areas: environment, biomedicine and health, economy and society, basic sciences and technology, and Culture — recognizes MIT Cecil and Ida Green Professor of Atmospheric Science Kerry Emanuel’s body of research on hurricanes and their evolution in a changing climate, as well as his effectiveness for communicating these issues. The annually bestowed Climate Change award acknowledges “both research endeavors in confronting this challenge and impactful actions informed by the best science.”
“By understanding the essential physics of atmospheric convection…he has unraveled the behavior of tropical cyclones – hurricanes and typhoons – as our climate changes,” cites the foundation’s conferring committee.
Throughout the 1980s and 1990s, after completing degrees at MIT and later joining the Department of Earth, Atmospheric and Planetary Sciences (EAPS) faculty, Emanuel pinned down the mechanisms behind hurricanes and how warming surface oceans fuel storms and increase intensity as the climate changes. This issue is of particular concern to humanity because, of the natural events, tropical cyclones cause many deaths and bring about high economic costs. Further research has probed connections between anthropogenic global warming and cyclone frequency, intensity, development time, and geographical expansion of hurricane occurrence.
The selection committee noted Emanuel’s exceptional theories and research that “has opened new approaches for assessing risks from weather extremes.” He has expanded this work by co-founding the MIT Lorenz Center, a climate think tank which fosters creative approaches to learning how climate works.
For Bjorn Stevens, BBVA Foundation committee chairman and Director of the Max Planck Institute for Meteorology, “it is hard to imagine an area of climate science where one person’s leadership is so incontestable.”
OpenSource science means, among other things, using OpenSource software to do the science. For some aspects of software this is not important. It does not matter too much if a science lab uses Microsoft Word or if they use LibreOffice Write.
However, since it does matter if you use LibreOffice Calc as your spreadsheet, as long as you are eschewing proprietary spreadsheets, you might as well use the OpenSource office package LibreOffice or equivalent, and then use the OpenSource presentation software, word processor, and spreadsheet.
OpenSource programs like Calc, R (a stats package), and OpenSource friendly software development tools like Python and the GPL C Compilers, etc. do matter. Why? Because your science involves calculating things, and software is a magic calculating box. You might be doing actual calculations, or production of graphics, or management of data, or whatever. All of the software that does this stuff is on the surface a black box, and just using it does not give you access to what is happening under the hood.
But, if you use OpenSoucre software, you have both direct and indirect access to the actual technologies that are key to your science project. You can see exactly how the numbers are calculated or the graphic created, if you want to. It might not be easy, but at least you don’t have to worry about the first hurdle in looking under the hood that happens with commercial software: they won’t let you do it.
Direct access to the inner workings of the software you use comes in the form of actually getting involved in the software development and maintenance. For most people, this is not something you are going to do in your scientific endeavor, but you could get involved with some help from a friend or colleague. For example, if you are at a University, there is a good chance that somewhere in your university system there is a computer department that has an involvement in OpenSource software development. See what they are up to, find out what they know about the software you are using. Who knows, maybe you can get a special feature included in your favorite graphics package by helping your new found computer friends cop an internal University grant! You might be surprised as to what is out there, as well as what is in there.
In any event, it is explicitly easy to get involved in OpenSource software projects because they are designed that way. Or, usually are and always should be.
The indirect benefit comes from the simple fact that these projects are OpenSource. Let me give you an example form the non scientific world. (it is a made up example, but it could reflect reality and is highly instructive.)
Say there is an operating system or major piece of software competing in a field of other similar products. Say there is a widely used benchmark standard that compares the applications and ranks them. Some of the different products load up faster than others, and use less RAM. That leaves both time (for you) and RAM (for other applications) that you might value a great deal. All else being equal, pick the software that loads faster in less space, right?
Now imagine a group of trollish deviants meeting in a smoky back room of the evile corporation that makes one of these products. They have discovered that if they leave a dozen key features that all the competitors use out of the loading process, so they load later, they can get a better benchmark. Without those standard components running, the software will load fast and be relatively small. It happens to be the case, however, that once all the features are loaded, this particular product is the slowest of them all, and takes up the most RAM. Also, the process of holding back functionality until it is needed is annoying to the user and sometimes causes memory conflicts, causing crashes.
In one version of this scenario, the concept of selling more of the product by using this performance tilting trick is considered a good idea, and someone might even get a promotion for thinking of it. That would be something that could potentially happen in the world of proprietary software.
In a different version of this scenario the idea gets about as far as the water cooler before it is taken down by a heavy tape dispenser to the head and kicked to death. That would be what would certainly happen in the OpenSource world.
You collect and manage data. You write code to process or analyze data. You use statistical tools to turn data into analytically meaningful numbers. You make graphs and charts. You write stuff and integrate the writing with the pretty pictures, and produce a final product.
The first thing you need to understand if you are developing or enhancing the computer side of your scientific endevour is that you need the basic GNU tools and command line access that comes automatically if you use Linux. You can get the same stuff with a few extra steps if you use Windows. The Apple Mac system is in between with the command line tools already built in, but not quite as in your face available.
You may need to have an understanding of Regular Expressions, and how to use them on the command line (using sed or awk, perhaps) and in programming, perhaps in python.
You will likely want to master the R environment because a) it is cool and powerful and b) a lot of your colleagues use R so you will want to have enough under your belt to share code and data now and then. You will likely want to master Python, which is becoming the default scientific programming language. It is probably true that anything you can do in R you can do in Python using the available tools, but it is also true that the most basic statistical stuff you might be doing is easier in R than Python since R is set up for it. The two systems are relatively easy to use and very powerful, so there is no reason to not have both in your toolbox. If you don’t chose the Python route, you may want to supplement R with gnu plotting tools.
You will need some sort of relational database setup in your lab, some kind of OpenSource SQL lanaguge based system.
You will have to decide on your own if you are into LaTex. If you have no idea what I’m talking about, don’t worry, you don’t need to know. If you do know what I’m talking about, you probably have the need to typeset math inside your publications.
Finally, and of utmost importance, you should be willing to spend the upfront effort making your scientific work flow into scripts. Say you have a machine (or a place on the internet or an email stream if you are working collaboratively) where some raw data spits out. These data need some preliminary messing around with to discard what you don’t want, convert numbers to a proper form, etc. etc. Then, this fixed-up data goes through a series of analyses, possibly several parallel streams of analysis, to produce a set of statistical outputs, tables, graphics, or a new highly transformed data set you send on to someone else.
If this is something you do on a regular basis, and it likely is because your lab or field project is set up to get certain data certain ways, then do certain things to it, then ideally you would set up a script, likely in bash but calling gnu tools like sed or awk, or running Python programs or R programs, and making various intermediate files and final products and stuff. You will want to bother with making the first run of these operations take three times longer to set up, so that all the subsequent runs take one one hundredth of the time to carry out, or can be run unattended.
Nothing, of course, is so simple as I just suggested … you will be changing the scripts and Python programs (and LaTeX specs) frequently, perhaps. Or you might have one big giant complex operation that you only need to run once, but you KNOW it is going to screw up somehow … a value that is entered incorrectly or whatever … so the entire thing you need to do once is actually something you have to do 18 times. So make the whole process a script.
Aside form convenience and efficiency, a script does something else that is vitally important. It documents the process, both for you and others. This alone is probably more important than the convenience part of scripting your science, in many cases.
Being small in a world of largeness
Here is a piece of advice you wont get from anyone else. As you develop your computer working environment, the set of software tools and stuff that you use to run R or Python and all that, you will run into opportunities to install some pretty fancy and sophisticated developments systems that have many cool bells and whistles, but that are really designed for team development of large software projects, and continual maintenance over time of versions of that software as it evolves as a distributed project.
Don’t do that unless you need to. Scientific computing often not that complex or team oriented. Sure, you are working with a team, but probably not a team of a dozen people working on the same set of Python programs. Chances are, much of the code you write is going to be tweaked to be what you need it to be then never change. There are no marketing gurus coming along and asking you to make a different menu system to attract millennials. You are not competing with other products in a market of any sort. You will change your software when your machine breaks and you get a new one, and the new one produces output in a more convenient style than the old one. Or whatever.
In other words, if you are running an enterprise level operation, look into systems like Anaconda. If you are a handful of scientists making and controlling your own workflow, stick with the simple scripts and avoid the snake. The setup and maintenance of an enterprise level system for using R and Python is probably more work before you get your first t-test or histogram than it is worth. This is especially true if you are more or less working on your own.
Another piece of advice. Some software decisions are based on deeply rooted cultural norms or fetishes that make no sense. I’m an emacs user. This is the most annoying, but also, most powerful, of all text editors. Here is an example of what is annoying about emac. In the late 70s, computer keyboards had a “meta” key (it was actually called that) which is now the alt key. Emacs made use of the metakey. No person has seen or used a metakey since about 1979, but emacs refuses to change its documentation to use the word “alt” for this key. Rather, the documentation says somethin like “here, use the meta key, which on some keyboards is the alt key.” That is a cultural fetish.
Using LaTeX might be a fetish as well. Obliviously. It is possible that for some people, using R is a fetish and they should rethink and switch to using Python for what they are doing. The most dangerous fetish, of course, is using proprietary scientific software because you think only if you pay hundreds of dollars a year to use SPSS or BMD for stats, as opposed to zero dollars a year for R, will your numbers be acceptable. In fact, the reverse is true. Only with an OpenSource stats package can you really be sure how the stats or other values are calculated.
This book focuses on Python and not R, and covers Latex which, frankly, will not be useful for many. This also means that the regular expression work in the book is not as useful for all applications, as might be the case with a volume like Mastering Regular Expressions. But overall, this volume does a great job of mapping out the landscape of scripting-oriented scientific computing, using excellent examples from biology.
Mastering Regular Expressions can and should be used as a textbook for an advanced high school level course to prep young and upcoming investigators for when they go off and apprentice in labs at the start of their career. It can be used as a textbook in a short seminar in any advanced program to get everyone in a lab on the same page. I suppose it would be treat if Princeton came out with a version for math and physical sciences, or geosciences, but really, this volume can be generalized beyond biology.
Stefano Allesina is a professor in the Department of Ecology and Evolution at the University of Chicago and a deputy editor of PLoS Computational Biology. Madlen Wilmes is a data scientist and web developer.
As is the case with the other kits, the Solar System includes a book, a large format big flat thing to which one might attach stickers, stickers, and a unique on-topic object, in this case, those cool stars you can attach to your ceiling or walls, and they glow in the dark. Continue reading The Solar System from The Smithsonian→
This is not a book that fully explores the alliance and overlap between war and makers of war on one hand and science and scientists on the other. Authors Neil deGrasse Tyson and Avis Lang focus on one part of that relationship, the link between astrophysics and related disciplines (really, astronomy at large) and the military.
Back when I was working in or near the Peabody Museum, in Cambridge, the museum’s assistant director, Barbara Isaac, hired me to work with the NAGPRA database. NAGPRA was the North American Graves Protection and Repatriation Act. Ultimately, large swaths of the Peabody Museum’s collection would be turned over, or some other thing done to it, as per the wishes of the various Native American groups associated with that material. Most of the work had already been done. But, Barbara is a meticulous person and wanted to make sure the dotting of each i and crossing of each t was double checked. So, I was one of two people charged with going over the printouts, on that old green and less green striped paper, bound in large blue cardboard books. Each line (or two) was an item or collection of items, with notes, and an indication of what was going to happen to the material. There were just a few options, but the basic idea was this: An item listed was either going to be returned to a tribal group, or not. My job was mainly to look at stuff that was not going to be returned and, given my ongoing scan of what was going to be returned, and my knowledge of North American prehistory, ethnography, and archaeology, to earmark things that said “do not return” but where maybe we should be returning it. So, for example, after noting that a particular South Dakota Lakota tribe would have this, and that, and this other, soapstone tobacco pipe returned to them, when I saw that the ninth pipe on the list, several lines down and all by itself, is labeled to not be returned, I’d earmark that. Nearly 100% of the time, that ninth pipe was just something that nobody wanted, or it didn’t really exist (not all museum databases are exactly accurate). But, it would be earmarked.
Many items on the list had information as to how the item had originally gotten to the museum.
Many, many items, especially items taken from Native Americans living in what was the frontier between about 1840 and 1900, were taken by medical doctors who, as we all know, also stood in for naturalists, or some kind of traveling scientist, on military and quasi military expeditions (Like Darwin).
And many of those items were taken for use as medical specimens.
We initially learned that Native Americans have a particular blood type because, in part, of studies done on blood stains on shirts of slain warriors, collected after various battles with the US Army units accompanied by such scientists. There are a few famous cases of Native American bodily remains, mostly but not all skeletal remains, sitting in the anatomy teaching rooms of this or that college. But a lot more, a lot not noticed by either historians or even the all seeing all knowing Wikipedia, are or were sitting in museums around the world. Collected, by scientists wearing military uniforms, on military ventures, with a scientific twist.
So the science-military link is not exclusive to astronomy and astrophysics.
I wrote elsewhere about the person I met who was taking Pentagon funding to build an object that would help cure cancer. An example of a scientist subverting the military funding process. And so on.
OK, my complaint.
The authors have two long chapters (and references elsewhere) covering the early history of human endeavor in general (not limited to military) and the evolution of astronomy, mainly as it related, over a very long period of time, to navigation. One chapter covers land, the other the sea.
Staring somewhere along the way in each chapter, we get a very nice, well done, and pretty full description of the process of humans learning about the stars, about the earth and how to find one’s way, etc. But prior to that, the authors do what so many authors do and I so much dislike. I’ve written about this before. We get a version of human prehistory, and indeed, current human variation (or at least, ethnographically recent), that is bogus. For example, the authors speak of the first modern humans wandering around in the Rift Valley of Africa. There is no evidence that modern humans evolved there. Using just the archaeology, southern Africa is a more likely origin, and the physical anthropology record is simply incomplete. There are early fossils there, but that is because the rift valley is and was a big hole that made fossils. The entire rest of the continent is big, and the evolution probably happened there, not in the rift.
Similarly, ethnographic variation we see in the present and recent times is stripped out. For example, most rain forest dwelling foragers are not known to have a sky oriented cosmology, or to use the sky for much information about seasonal change in ecology, or navigation. And, there have always been a lot of rain forest dwelling foragers.
Suman Seth is associate professor in the Department of Science and Technology Studies, at Cornell. He is an historian of science, and studies medicine, race, and colonialism (and dabbles as well in quantum theory). In his new book, Difference and Disease: Medicine, Race, and the Eighteenth-Century British Empire, Seth takes on a fascinating subject that all of us who have worked in tropical regions but with a western (or northern) perspective have thought about, one way or another.
As Europeans, and Seth is concerned mainly with the British, explored and conquered, colonizing and creating the empire on which the sun could never set no matter how hard it tried, they got sick. They also observed other people getting sick. And, they encountered a wide range of physiological or biosocial phenomena that were unfamiliar and often linked (in real or in the head) to disease. A key cultural imperative of British Colonials as to racialize their explanations for things, including disease. The science available through the 18th and 19th century was inadequate to address questions that kept rising. Like, why did a Brit get sick on his first visit to a plantation in Jamaica, but on return a few years later, did not get as sick? If you have a model where people of different races have specific diseases and immunities in their very nature, how do you explain that sort of phenomenon? How might the widely held, or at least somewhat widely held, concept of polygenism, have explained things? This is an early version of the multi-regional hypothesis, but more extreme, in which god created each type of human independently where we find them, and we are all different species. (Agassiz, with his advanced but highly imperfect geological understanding, thought the earth was totally frozen over with each ice age, and repopulated with these polygenetic populations of not just humans, but all the organisms, after each thaw).
Seth weaves together considerations of slavery and abolition, colonialism, race, geography, gender, and illness. This is an academic book, but at the same time, something of a page turner. Anyone interested in disease, colonial history, and race, will want to re-excavate the British colonial world, looking at disease, illness, and racial thinking, with Suman Seth as your guide. I highly recommend this book.
I no longer call myself a skeptic. Well, actually, I probably never really did, but now I’m more explicit about that. Why? Two reasons. 1) Global warming and other science deniers call themselves skeptics, and I don’t want any confusion. 2) The actual “skeptics movement” is described as…
…a modern social movement based on the idea of scientific skepticism (also called rational skepticism). Scientific skepticism involves the application of skeptical philosophy, critical-thinking skills, and knowledge of science and its methods to empirical claims, while remaining agnostic or neutral to non-empirical claims (except those that directly impact the practice of science). The movement has the goal of investigating claims made on fringe topics and determining whether they are supported by empirical research and are reproducible, as part of a methodological norm pursuing “the extension of certified knowledge”. The process followed is sometimes referred to[by whom?] as skeptical inquiry.
That’s all nice and all, but I discovered that the actual skeptics movement is made out of people not quite so cleanly guided by a philosophy, roughly one third of whom are not really skeptics (such as Penn Jilette and James Randi, who allowed their libertarian philosophy to drive “skepticism” of anthropocentric global warming long after the scientific consensus was established), “mens rights activists” (MRAs) who vigorously attacked anyone speaking out in favor of women’s rights, against rape, etc., and #MeToo movement poster boys, who have for years used skeptical conferences as their own private meat markets.
Besides, I’m an actual scientist, so I can be a fan of science without having to be a fanboy, which makes it easier for me.
I started writing publicly, blogging, partly to be an on-line skeptic, to take on politically charged topics, especially as related to evolutionary biology, but other areas of science as well (and more recently, climate change), addressing falsehoods and misconceptions. But I very quickly discovered that there multiple and distinct kinds of “skepticism” make up the larger conversation.
There is a lot of very low level, knee jerk skepticism that is little more than uninformed reactionism, based on, at best, received knowledge. That is about as unskeptical as it gets. The Amazing Randy says Global Warming is nothing other than natural variation. Therefore, I will believe that. Uncritically. Some of this is what I long ago labeled as “hyperskepticism.” This is where potentially valid skepticism about a claim is melded with hyperbole. “There is not a single peer reviewed study that shows the bla bla bla bladiby bla” coming from the mouth of a person who has never once even looked for a peer reviewed study about any thing. They hyperskeptic may create entire categories of things that include claims worthy of debunking, and put all of the thing into the debunked category even if they are not.
A fairly benign example of this relates to CAM medicine. “CAM” refers to “complementary and alternative medicine” like acupuncture, rolfing, and the like. These are mostly forms of treatment that have no basis in science, and probably don’t do anything useful even if they sometimes cost real money. Hypersketpics put all CAM into the same category and light a match to it. But, there is a subset of CAM that is legit … the very fact that I wrote that sentence just there will disqualify me, and my entire post, and everything I ever say — there will be comments below that say “I stopped reading when you said “there is a subset of CAM that is legit”. OK, hold on a second, count to four. One two thee four. Now that all the hyperskeptics have gone off in a huff I can continue … and I can give you an example. There are people who undergo regular, uncomfortable, sometimes painful or sick-making treatments as part of their normal medical routine. Chemotherapy, dialysis, that sort of thing. We know that the quality of an individual’s life can be improved, their stress levels, reduced, and thus, probably, the outcome of their treatments improved or made less complicated, if the environment in which they get the treatments are more comfortable. This is why dentists put ferns and pictures of the ocean in their waiting rooms. There is evidence to suggest that surroundings should be considered in design of treatment rooms, waiting room, etc. (See for example, Brown and Gallant, 2006, “Impating Patient Outcomes Through Design: Acuity Adaptable Care/Universal Rom Design. “Critical Care Nursing Quarterly. 29:4(326-341) and Ulrich, Zimring, and Zhu, 2008, “A Review of the Research Literature on Evidence-Based Healthcare Design. HERD 1(3). They hyperskeptic wants divide the world into evidence based double blind study proven and everything else, with everything else being always wrong in all ways. (Perhaps I exaggerate a little, but only for the irony.) This concept, of considering room and environmental design, now standard, did exist before CAM (those dentists and their ferns) but the study an implementation of stress reducing design as we now know of it comes from the CAM movement. What is needed is not closing down CAM, but making it accountable. It would probably get much smaller if that happened, but what is left of it would be useful.
Four others contributed to this volume, Bob Novella, Cara Santa Maria, Jay Novella, and Evan Bernstein.
I do not agree with everything in this book. For example, although the discussion of placebo effect is excellent, I have a different take on it. I like to divide the effect up into different categories than I do, and I want to make a more explicit connection between the phenomenon called placebo effect and the role and meaning of a control. But for the most part, every single one of the more than 50 topics covered in this book is well treated, informative, and enjoyable to read. (See what I did there? I was a little skeptical of the book, so now, you know it must be good!)
Do get and read this book, get one for a friend for a holiday gift, and enjoy. But right now, before you even do that, to tho the Amazon page and find the negative reviews. There are only two now (the book just came out) but they are a hoot.
There is a great deal of overlap and integration between government agencies, private corporations including Big Pharm and Big Ag and Big Whatever, university and other research institutions, and the scientist and others who work in these places.
This topic is addressed in the latest episode of the science podcast Ikonokast, which also includes details about a recent minor scandal involving GMO research and squirrels. And maybe bears.
Quotes by Charles Darwin are not just the stuff of memes. Even the fake quotes. They can be the center of long arguments, or at least, they can significantly augment the arguments. For example, did you know that while Darwin never used the term “missing link” he did talk about missing links quite a bit, missing links are central to his thinking about evolution, and all those writers of today who claim that we must never speak of missing links are misguided? Continue reading Darwin Quotes, Assembled→
I know a lot of you are interested in global warming/climate change, so you need to know that this book is not mainly about that (but it is covered). Rather, this book is the Rosetta Stone that allows you to connect a general understanding of the planet (it is round, it spins, it has an atmosphere that includes water vapor, and tends to reside between -50 and +50 degrees C, etc.) and the person on the TV talking about air masses going up and down and what is going to happen during “the overnight” and “the overday” and such. Continue reading Making Sense of Weather and Climate→
This is a repost of an item I put up in 2013 based on some interesting scientific research. Today, I was told by Google that if I do not take the article down, I will lose my ad sense qualification. Google and companies like Google are giant behemoths that do not have humans to whom one can talk when they do something boneheaded like this. So, I’ll unpublish the original item and post it here with a change in title. Also, words that might be interpreted by an unintelligent robot at Google as violating policy have been changed. Continue reading Measurements of the human male kakadodo organ, does it matter and why?→
At present, the evidence suggests that life may have existed in the past on Mars, or not. However, the scientific consensus is that we assume life never arose on Mars, and will continue to do so until evidence pops out and bites us in the mass spectrometer.