How Bad Science Becomes The Science: Climate & Public Health Example

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In my not-yet-banned talk on the top five reasons Science is broken, I list as number two the Expansion Team Effect and the corruption of money. (Woke & DIE is first.)

Money swells and pushes into Science like a tsunami, forming pools which become the breeding grounds of scientists. The larger the pools, the more scientists are formed. There has been so much money forced into the system these last seventy years, you can now find a scientist to support just about any contention you want to push, and this is of course easier the more money and power you have to support it.

When the amounts of money become large enough they become a sort of proof that the areas of study are both legitimate and true. For one, because that money is funneled to discover scientists who support those areas. For two, the money itself is an argument, sort of like how journalists hang onto Bill Gates’s words. They hope, in some mysterious and vague way, that they’ll be the beneficiary of his largess.

It’s not that scientists come running with the herd because they hope to become rich. It’s because they, being human, naturally run with the herd, and herds have to find life-giving water (money) like everybody else.

Once the herd grows, it self-perpetuates. The size itself is all it takes to convinces others that all is well. Research, as it is called, bursts forth, and like the herd itself, the sheer massiveness of it is enough to convince others of its veracity.

Enter the serial herd leader The Lancet and their herd-reviewed “The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels” by Marina Romanello and a herd of others.

This now-annual report is like the paper we looked at yesterday: advocacy masquerading as science. Here’s a quote from the opening:

Climate change is undermining every dimension of global health monitored, increasing the fragility of the global systems that health depends on, and increasing the vulnerability of populations to the coexisting geopolitical, energy, and cost-of-living crises.

It is not logically impossible that if the weather changes that every dimension of global health will be “undermined”, but it is absurd to say so. (A changing climate means nothing in itself; it’s the weather that counts; people experience weather, not climate.)

We’ve used this example before, humble as it is, but it is no less valuable for that. Every winter tens of thousands of northerners trek southward to Florida and Arizona. And they do this to gain tens of degrees of temperature. Not just a fraction or two of a degree as promised by global warming models, but tens of degrees.

Raw heat. And they do this for their health. If just increasing temperatures slightly was enough to be as deleterious to health as Lancet’s Experts would have it, we should find a huge increase in death rate as we moved from Green Bay to Tampa Bay. We do not.

Now how could such simple, easily verifiable, easily seen facts have been ignored by this herd of Experts?

Easy! Models. Which they were forbidden to question or doubt, because doing so makes one into a denier. These health Experts must accept the word of climate Experts, which means taking their climate models as is, without possibility of doubt. You will search for days, and search in vain, to find even a hint of a whisper of a rumor of a doubt.

The climate models then become input, usually directly but sometimes indirectly, into the health Experts’ models. And, of course, climate Experts will not, and must not, question these health models (with their wee p-values galore). Nor must any other Experts outside health and climate question them, either. Questioning just isn’t on in an expertocracy.

Naturally, what I call the multiplication Of uncertainties, is completely ignored. Think: the data that goes into both climate and health models has some uncertainty attached (more in the former case). This is ignored. The climate models have uncertainty. That is ignored. The models’ output is fed into health models as if it is perfect.

The end result should be a multiplication of uncertainties, such that there should not be much, if any, trust in the long string of modeling.

But, again, doubt is not allowed. Doubt is disloyal, you denier. The enormous mass of research becomes its own proof of its correctness. The whole becomes an Appeal To Swelling Bureaucracy, a fallacy, but a beloved one.

One example to prove my contention:

Insufficient climate change adaptation efforts have left health systems vulnerable to climate change-related health hazards. Only 48 of 95 countries have assessed their climate change adaptation needs (indicator 2.1.1) and only 63% of countries reported high to very high implementation status for health emergency management in 2021 (indicator 2.2.4). Increasing adaptation to climate change has the potential to simultaneously improve the capacity of health systems to manage both future infectious disease outbreaks and other health emergencies (indicator 2.3.1).

If that isn’t as pure a bureaucracy as you’ve seen, then you don’t know bureaucracy.

And just how easy it is to lie to oneself:

This is “Change in the percentage of people reporting moderate to severe food insecurity” because supposed warming. But food insecurity does not mean lack of food! Or lack of eating, or starvation, or anything like that.

It is a derived measure, not a real one; it is a model. It is only meant as proof of how bad things are known to be in the minds of researchers, when they can’t find actual bad things, like famines. So they make things like this up, to reassure themselves they are right.

Here’s the contrast, the actual corn production through time in major countries (the same signal is in rice, nuts, et cetera; see the link).

The model, which is “food insecurity”, becomes realer than Reality. The Deadly Sin of Reification has been committed.

And sworn to by a herd of scientists.

Buy my new book and learn to argue against the regime: Everything You Believe Is Wrong.