I. #TheScience is not Well.

I’m going to begin with two claims about the state of #thescience in the United States. I use the hashtag advisedly because one of my assertions is that the majority of what passes for science in the current zeitgeist is generally non-science, or, most charitably, bad conjecture, if we’re properly classifying the current crop of pop-culture fads attempting to pass themselves off as real Science (yes, I’m looking at you AGW and Covid-19 Scam-demic). To begin with, here are my two claims:

1. The current state of science in the United States is…unwell. This is not to say that there isn’t some good science being done to which one can point, but the measure of the health of science in any age isn’t simply what things are invented, or what new technologies may be patented that lead to the ease of living of the species. I will take this up in detail below, but for now I make the claim that science in the U.S. is in a deplorable state, due to a confluence of factors that began from within science itself, but spread to other areas, such as the Law.

2. A direct consequence of #1 is also a piece of evidence and a separate problem: the odds are extremely high that anything you’ve read recently in a mainstream media science piece, no matter how ‘peer reviewed,’ is garbage. My conservative estimate is that it’s about 80/20 in favor of garbage.

Before I can properly begin my argument, however, I must first define what I mean by science, as well as a number of related concepts in order that a metric is established by which I can show where things are currently awry, as well as the how and why that happened. So, I’ll leave these here and return to their proof below. Without trying to be pedantic, I ask some forbearance on this reiteration of the basics. Blame public education – I do!

II. What is Science?

It is a methodology for modeling the universe; nothing more, and certainly nothing less. To be more precise, science is the process by which we develop models of the real world with predictive power. Science proceeds on one giant underlying assumption: that there are “rules” and “order” to our material world, about the nature of… well, Nature, that can be discovered, and modeled, by mankind. In some cases, like Newton’s Law of Gravity, the model can be so powerful, with such mathematical precision, that predictions can be made about the future time, position, and even energy state of bodies on Earth or in space.

Science is also how you have been understanding the world from the moment your senses turned on and became capable of taking in and processing stimuli. You have been producing models from inductive reasoning, using heuristics and other cognitive tools, to come to grips with the stunning array of information presented to you, then updating these models as more data comes in - confirming some hypotheses, discarding others, modifying yet others, limiting the domain and range of some theories… What we traditionally learned as the scientific method is an articulation of that mental machinery that churns out models of how the world works.

You’ve probably seen something like this as a rough definition of the “Scientific Method” at some point in your life:

Observe

Ask a question

Develop a testable hypothesis

Experiment – TEST your hypothesis

Analyze the data/results

Make a Conclusion

This is an iterative process and can be entered from different points along the path. You may have a simple question that nags at you, or you may have a particularly well-developed theory – as in the case of Einstein’s gravitational waves – but in either case, if you don’t have a testable hypothesis, one that has measurements and criteria for validation, the problem remains. As this example above (hopefully) illustrates, Science does not necessarily yield absolute, universal truth… at least not NOW, or maybe not on the first go-around, or maybe not ever, which is why we have categories for scientific models based upon their predictive strength.

Here are the criteria and definitions for science:1

1. Rational argument must be the zeroth axiom. This has a number of corollaries, but the most important of which is “consistency” or “non-contradiction” - for example, something can’t be both A and not A at the same time.

2. Science is the source and repository of Man’s objective knowledge.

3. Scientific knowledge is siloed in models, which are graded by the strength of their predictive power, from conjecture, to hypothesis, to theory, and then law.

4. Observations are a registration of the real world on our senses or sensing equipment, BUT they do not become “scientific facts” until we can measure them against some standard scale. This is a non-trivial requirement.

5. Scientific facts, the foundation of all model building and testing, are measurements with an established accuracy.2

6. Models map a current fact to a future unrealized fact as a prediction. Another way of saying this is that a prediction is a forecast of a measurement.

7. Validation and method are entirely independent. Whether the prediction came by inspiration or perspiration is irrelevant - what validates the conjecture or hypothesis is its predictive power.

8. An important corollary to number 7 is that explanation of cause is in the eye of the beholder.

9. The application of science to public policy with unvalidated models is unethical.

III. What Isn’t Science.

With this as the foundation upon which we build, it’s evident that the #IFL Science! Crew traffics in something more akin to a religion – scientism, but certainly not science. For example, nowhere in our schema above for science does the word ‘consensus’ appear. For some of us of a certain age, the word ‘consensus’ was never taught in conjunction with science; the word was never spoken in a science classroom. This is because consensus is not a part of the scientific method and its adoption into #science has a direct correlation to the cheapening of real science. Science cares nothing for votes or popularity; either a model delivers predictions that can be tested and measured, that is validated or not, or it is either (1) an incorrect model, or (2)  isn’t science at all. This is where the popularity of certain ideas, mingled with the need for funding for continued research, can lead to bad outcomes. This is at its worst when popularity includes the government concretization of ideas.3

In truth, the new #science is the politicization of science, and it is, unfortunately, nothing new. The Lysenkoism of the 1930’s charts its rise quite nicely with the fall to our current state of scientific illiteracy and innumeracy. Science in service to the state is one (among many) of the defining characteristics of statist systems of government, such as socialism, communism, fascism, and even corporatism, of which we have more than our share. AGW is simply the newest version of Trofim Lysenko’s politicized science that seeks its answers not in seeking truth, but in power and control, in popularity, populism, and the censorship of competing ideas, all of which undermines the very foundations upon which science is built. Science proceeds on the refinement of models: as Einstein slightly narrowed the domain of Newton’s Laws of Motion to more accurately model what happens when the v = velocity in Newton’s equations approaches c, the speed of light. It is easy to forget that Relativity was a refinement that was four-hundred years in the making. Or to cast it in a slightly different light, five years before the Puritans started the Salem Witch trials, Isaac Newton published the Principia. Newton’s models withstood four-hundred years of human progress in science and, even then, Einstein only narrowed them for a few special cases, including objects traveling at light speed.

Einstein left us with numerous other models, some of which bear his name, on the basis of the profundity of his contributions. The same is true for Otto Warburg: the Warburg effect is what occurs when you drink a radioactive sugar and then have a PET Scan that “lights up” the cancerous tumor cells, as those cells preferentially uptake the glucose over surrounding healthy, non-cancerous cells.4 It’s why Glenn T. Seaborg had an element of the periodic table named after him, (Seaborgium, Sg – 106), while he was still alive. Seaborg and Edwin McMillan discovered Plutonium and both won the Nobel Prize in Physics in 1951. That line of transuranium elements on the bottom of the periodic table – the actinide series? Seaborg, as well.

IV. Industrial Science v. Academic Science

As I noted in the opening piece, Jeff Glassman joined Hughes in the late-1960s, after he finished his PhD in Engineering from UCLA. Like many of his fellow graduates, given their interests and multiple degrees in electronics, applied mathematics, applied physics, and communication and information theory, employment was plentiful in the corridor north of Los Angeles that contained the heart of the United States’ burgeoning aerospace/defense industry, chief among them Hughes Aircraft Company. Jeff’s family had a legacy at Hughes and it was no accident that he would spend half of his three decades at Hughes as the Division Chief Scientist for both the Missile Development and Microelectronics Systems Divisions.

Among the projects to which Jeff contributed significantly was the AWG (pronounced in military slang like it rhymes with dog) Nine (AN/AWG-9) radar. That caught my attention when I learned about it from his son. I was an attack helicopter pilot in the 1990’s, but I grew up keenly interested in American military aviation of the 1980’s. The AWG-9 radar, and the plane that ultimately carried the famous missile guided by it, would be featured in “Top Gun.” The F-14 Tomcat was the final result of a long acquisition process to find a fighter-interceptor capable of taking advantage of the standoff distance of both the radar and the vaunted Phoenix missile. While the Iranians claimed to have splashed over 60 Iraqi MiGs with the Phoenix and the AWG-9, the only three ever admitted to being fired by U.S. fighters all missed their targets.

Regardless of its record, the chief deterrent effect of it lay in its effect on pilots on the other side of the Cold War. Almost all fixed-wing aircraft, and even some rotary-wing, have devices much like the ones you use in your car to detect police radar. The detectors work in specified frequency bands and detect the radiated electromagnetic energy that is being sent at your car by the cop’s radar gym. In return you get a tone, or a spike, on your detector. The ones in planes are using fundamentally the same principles, but the receivers will give returns for slightly more sophisticated radars, including a strobe for direction and strength of signal, up and including targeting radars, like the kind on missile seeker heads and their radars. These produce the “tone” that is now ubiquitous in modern aviation movies.

The old adage in dog-fighting is that ‘first in sight wins the fight.’ This is because in dog-fighting, the person in the higher energy state – typically the person at higher altitude – all other things being equal, can translate that extra potential energy into kinetic energy at some decisive point in the fight, usually as airspeed, sufficient to shoot down the opponent. For Soviet-bloc fighters going up against the Tomcat, its radar, and the accompanying Phoenix (AIM-54) missile, it meant getting a tone in our headset and a “lock” signal while you were still flying blind, unable to “see” anything, because that’s what happens when someone else’s radar can “outreach” yours. Hence, regardless of the U.S. record with the Phoenix missile, the AWG-9 likely helped keep the Cold War cold and gave the U.S. air superiority because it could see farther than anything the Russians had.

The AWG-9 and the accompanying technology for the missile seeker head, the ability for the missile to track multiple targets, to travel at supersonic speeds, and to be able to shunt fuel in turns at 5-6 times the force of gravity, is all Real Science at a high level, where getting it wrong means lives and truckloads of money lost. Notice also that all of that work was classified top secret or better and therefore not subject to “peer review” or “publication” in a science-y journal; and the mathematics behind some of those radars was at a level that a very small coterie of people were even qualified to comment on. Radar is, fundamentally, the ability to distinguish signal from noise among a radar return from roughly 75-100 nautical miles away.5 That is both a highly technical math problem, and extraordinary engineering problem. And none of that even begins to address the ability of the radar to acquire, track, and target multiple aircraft traveling at high velocities in different directions all intent on doing harm to the person sitting in front of that same radar while it moves through space on an F-14 doing 1.5 times the speed of sound.

Compare that to the absolute mountain of dreck that comes out of universities and has the imprimatur of “peer review” on it. Does this prove that whatever is being “studied” is true? Does it even make a prediction that can be tested for validation? What does it even mean to make a claim about the p-value of a paper’s data? We’re going to cover this scientism in detail down the road, but suffice it to say for now that I hope the above example dispels some of the halo effect around terms like “study”, “peer review”, or “p-value” as having anything to do with real science. All of these are entirely artifacts of academic (i.e. university) science, where what has primacy is the government grant trough and personal reputation, not predictive power of models.

I want to finish this first chapter on Science with a reminder of Science’s truth-seeking function. I also note here that Science is in this aspiration no different than the Law, or Literature, or, more broadly, all of good Art. Good comedy is funny because of how well it presents the Truth – or apparent Truths – typically in a striking, ironic, or unusual light. Shakespeare’s Romeo and Juliet was written in the context of 17th century Elizabethan drama, yet it has been remade time and time again, into West Side Story's Sharks v. the Jets, and many, many other variants. This isn’t because it’s false. Indeed, even Religion would be well included in this list of truth-seeking human endeavors and it serves (perhaps) as a reminder why the #IFL Science crew is so much closer to being a religion than to being scientists.