The Response to the Retraction

Keruru retracted his previous endorsement of the N/Nₑ argument, and much to my chagrin, he was right to do so: the derivation of Kimura’s substitution identity never needed Nₑ on either side of the algebraic equation. Supply is 2 in the census N, the fixation probability of a new copy is 1/(2N) in the same N, the two correctly cancel, and the fact that the effective population Nₑ was regularly written into the supply side was nothing more than careless notation by the authors of the biology textbooks over the years, it was not a hidden move by Kimura back in 1968. Thanks to this additional evidence that biologists can’t do math, this error means that it will be necessary to produce a 3rd Edition of Probability Zero to correct my mistake in this regard. Mea maxima culpa, dammit.

The second part of his post is not correct. He says zero fixations across the ancient-DNA window is exactly what neutral theory predicts, so the data cannot discriminate. The arithmetic holds given one number: Nₑ ≈ 10,000 — which comes from θ = 4Nμ, which presupposes k = μ, the identity under test. He has retracted an empirical falsification of the clock by feeding in a parameter that the clock manufactures; Charlesworth flagged this exact circularity in 2009, and Keruru himself, two sections later, calls Nₑ “estimated from the quantity it is then used to explain.” He diagnosed the disease in Section 3 and caught it in Section 2. His chains carry it from the other side: they differ in offspring-number variance, never the contested axis, while his sweepstakes parent is drawn uniformly at random — setting the one parameter in dispute, the covariance between who breeds and what they carry, to zero by fiat.

And most crucially, his work never contains a census population. His own supply term at Nₑ = 10,000 gives 740,000 new mutations per generation; Kong et al. (2012) times the global birth rate gives 2.5 × 10¹¹, a factor of 330,000 — which does not touch his repaired algebra but demolishes the literature that algebra abandons. He rescued three lines and orphaned fifty-seven years of applying them. Then he names frequency drift as “the analysis that should have been run.” As it happens, I ran it while writing the first edition of PZ: the Allen Ancient DNA Resource, 1,372 Neolithic against 680 modern Europeans, has rs35619459 moving 29.3% → 91.3%, a twelve-sigma excursion against his own stated expectation, and a drift-variance Nₑ near 2 rather than ten thousand. The signal is the size of a population turnover — structure, non-exchangeability, the very thing his model pinned at zero.

Nevertheless, I owe him a considerable debt of gratitude and not just for pointing out my mistake. For, as we’ve repeatedly seen over the course of this project, the identification of one particular flaw, and the subsequent process of correcting it, has led to a new discovery. The mechanism he reached for in the second part— that the fixation time is so enormous almost nothing destined to fix finishes inside the window — is exactly right, and it turned out to be the most productive thing in the exchange. He relied upon it to argue the data cannot test the clock; but when it is reversed, that same mechanism does not protect the clock, it provides a time limit. If the chance a destined allele completes within T generations scales as exp(−π²Nₑ/T), then k = μ cannot be realized at all in any population large enough for that transit to run 4Nₑ generations — and at census population scale that exponent becomes a ceiling on population size, about ten thousand for a large vertebrate, above which gennetic drift is not slow but entirely shut down.

That is the argument of my new paper entitled The Hard Limits of Fixation Through Genetic Drift which you can read at the preprint site, and it exists only because Keruru identified my mistaken identification of Kimura’s algebraic non-error.


ABSTRACT: The Hard Limits of Fixation Through Genetic Drift,

Kimura’s neutral substitution rate, k = μ, is a steady-state identity: it holds only after a population has held one size for the roughly 4Nₑ generations a neutral allele needs to drift from a single copy to fixation. That transit has never been checked against the number of generations that species lineages have actually had. Checking them yields a hard ceiling on population size, X = (Vₖ + 2)·G/16 — reproductive variance and lineage generations alone, with no mutation rate, no coalescent quantity, and no fitted constant. For a large, long-lived vertebrate the effective ceiling falls to about ten thousand individuals. Above it, a neutral allele’s chance of fixing within the generations its lineage will ever have is not merely small but exponentially small, of order exp(−π²Nₑ/G) — for humans at current size, about one in ten to the seventy-eight-millionth. Humans number more than eight billion, the better part of a million times over the line; the African elephant, endangered at four hundred thousand and falling, is still forty times too abundant for drift to complete to fixation. No vertebrate population is simultaneously large enough to persist as a species and small enough to fix a neutral allele by drift. What substitution these abundant populations show is not produced at their current size; it is residual drainage from the smaller populations they descend from. The domain of k = μ is confined to demographic conditions that no non-endangered species is capable of meeting.

DISCUSS ON SG


The Irrelevance of k = μ 

Keruru has uncovered some interesting things in his review of the mathematics underlying Kimura’s cancellation, which I have shown is a) incorrect for most species and b) a Portuguese mathematician has shown to be mathematically incorrect:

In February I argued that the mathematics underwriting neutral theory was broken. The argument had two legs. The first, drawn from a preprint I had read approvingly, held that Kimura’s substitution-rate identity k = μ rests on an equivocation: that the effective population size N<sub>e</sub> is made to stand for two incompatible quantities — a mutation-supply term and a drift term — which are then cancelled against each other. The second was empirical: an ancient-DNA analysis reporting essentially zero allele fixations across a million loci in the 240 generations separating Early Neolithic Europe from the present.

Both legs have given way. I want to set out how, because the manner of their failure is more interesting than the original claim, and because the instinct behind the essay turns out to have been sound while its aim was off by about thirty degrees.

The equivocation that isn’t

Written as k = 2N_e μ × 1/(2N_e) = μ, the objection looks devastating. Mutation supply is a biochemical fact about replicating cells and scales with the number of individuals actually reproducing. Drift is a statistical fact about sampling variance and scales with something much smaller and considerably slipperier. Two different numbers wearing the same symbol, cancelled against each other, yielding a result that has calibrated half of molecular anthropology.

The trouble is that the derivation does not require N<sub>e</sub> in either position, and the textbook notation is simply careless.

Mutation supply is 2Nμ with N the census count — mutations occur in gametes, and every reproducing individual contributes gametes. The fixation probability of a single new neutral mutant is its initial frequency, and one new copy among 2N copies has frequency 1/(2N) — census again. The two census terms cancel. N<sub>e</sub> never enters, and so cannot be equivocated upon.

Why is the fixation probability equal to the initial frequency? Not by assumption. Under neutrality the expected change in allele frequency each generation is zero, which makes the frequency a bounded martingale; a bounded martingale that must eventually be absorbed at 0 or 1 has absorption probability at 1 equal to its starting value. That is optional stopping, and it is a theorem rather than a modelling convention.

The claim is testable without any diffusion approximation at all, using exact finite Markov chains. I built two. The first is ordinary Wright–Fisher. The second is a sweepstakes model: half the time, half the population is replaced wholesale by clones of a single randomly chosen parent — reproductive skew of the kind documented in Atlantic cod and Pacific oysters, wildly outside anything Wright and Fisher had in mind. Measured from heterozygosity decay, the sweepstakes chain has an effective size 5.6 times smaller than the Wright–Fisher chain at identical census size.

The fixation probability of a single new mutant in both chains: exactly 1/(2N_census), to fifteen decimal places.

N<sub>e</sub> can be dragged around by a factor of six and the fixation probability does not move. It follows that my own demographic work on the collapse in variance of reproductive success — a real finding, and one I still stand behind — does not touch the substitution rate. It changes coalescent depth, standing diversity, and every estimate of N<sub>e</sub> derived from them. It leaves k = μ exactly where it was.

First, the problem with Kimura turns out to be even more significant than I originally believed. I was pretty sure that Keruru had been led off track the moment that he mentioned the word “martingale” because that’s the retreat that every AI, including both Claude Athos and the entire Red Team, initially made. You cannot use AI to provide you the answer; you have to know at least the correct shape of the answer before you ask the AI.

I’ll post my full response to Keruru later this week. His work here is very far from valueless, as I will demonstrate, but it is nevertheless incorrect due to his failure to anchor the abstract mathematical elements in material reality. This also, rather fortuitously, demonstrates the importance of the Triveritas, as any one link in the triad is capable of serious error when allowed to operate outside the bounds of the other two.

But the epicycle wasn’t elsewhere. There are multiple epicyles.

k = μ is a correct baseline for asking whether something other than drift is happening. But it is not a rate at which anything can be observed to occur in a sexual, age-structured, demographically non-stationary population — which is every organism anyone cares about.

DISCUSS ON SG


The NFL and CTE

It’s not looking great for ex-football players:

A new study from The BMJ finds that at least 24.5 percent of all former NFL players who died between 2016 and 2021 had CTE. Of the 878 who died during that period, at least 215 had CTE. Of the remaining 663, 643 of the players’ brains were not studied for the presence of CTE. Which means that the real percentage could be much higher.

It almost certainly is. A friend of mine married, and eventually divorced, a former college football player whose personality and behavior changed drastically over time. She suspects that he’s got CTE, but of course there is no way of knowing for sure until he dies. I wouldn’t be at all surprised if it turns out that more than half of the former NFL players had it.

It’s a violent game. And the players should be given every opportunity to know what they’re getting into.

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The Manufacture of Alzheimer’s

Pre-Covid, it was very hard to believe that the entire medical establishment was actually trying to kill people. Now that 75 percent of the population has been injected with heart attack and turbo cancer machines due to a man-made disease that never threatened the vast majority of the population, people are starting to look a little harder at other serious medical issues:

DOCTORS ARE LITERALLY MANUFACTURING ALZHEIMER’S

Dr. Joel Wallach: “Alzheimer’s is a PHYSICIAN-CAUSED disease.”

Your brain is 75% myelin insulation. And myelin is 100% cholesterol.

Statins shred that cholesterol — stealing 75% of what your brain desperately needs to stay insulated and functional.

No myelin = no brain. That’s why Alzheimer’s went from virtually unknown to the 4th leading killer over age 65.

They told you to lower your cholesterol…while your brain was starving for it.

This isn’t “aging.” This is medical malpractice on a massive scale.

This observation is just anecdotal, but I only know three people in the two previous generations who had some form of dementia before they died. However, all three of them were on statins…

At this point, you couldn’t pay me to take any medication or prescription that doesn’t have an immediate, obvious, and observably beneficial effect. No matter what the numbers on the printout say.

DISCUSS ON SG


Chalub and the Kimura Cancellation

In 2022 a Portuguese mathematician named Fabio Chalub published a paper solving the neutral Kimura equation called Solution of the Neutral Kimura equation with two integral constraints. It is not a critique of neutral theory. Chalub gives no sign that he is even aware that anyone is contesting neutral theory. He is a specialist in degenerate partial differential equations doing what specialists do, which is testing the assumption that a well-known equation actually has the solution everyone has assumed it has.

Chalub opens by pointing out something that seventy years of science textbooks have failed to notice. The classical solution to the neutral Kimura equation — the one Kimura wrote down in 1955, the one reproduced in Crow and Kimura and in every graduate course since — decays to zero. All of the probability drains out of the interior of the interval and nothing is left. As a description of a population, it is nonsense: the alleles have to go somewhere, which is either fixation or extinction. The standard solution does not track them. It simply stops accounting for them. To fix this, Chalub adds two point masses at the boundaries, one representing the fraction of populations in which the allele has fixed and one representing the fraction in which it has been lost. Fine. Now comes the interesting part.

Nothing in the Kimura equation determines how the escaping probability splits between those two boundaries. The equation degenerates at both endpoints — the diffusion term goes to zero there — and there is no condition inside the mathematics that says how much goes to fixation and how much goes to extinction. The equation upon which the entire molecular clock is built does not provide the one number that it needs. So Chalub supplies the number from outside, in the form of two constraints he imposes by hand before solving anything. The first is that total probability is conserved. The second is that the average allele frequency never changes, which is not a fact about diffusion but an inherited assumption from the Wright-Fisher model, the very assumption that has been successfully disputed by me and three scientists before me. Impose that assumption and the fixation probability comes out equal to the starting frequency. Of course it does. Chalub writes in the constraint before he solves anything, recovers it as a result two pages later, and he says plainly that the constraints are inherited from the discrete process.

He has no reason to be aware of what he has demonstrated. The diffusion machinery is invoked throughout the literature as independent confirmation that a neutral allele fixes with probability equal to its frequency. Chalub shows the machinery cannot confirm any such thing, because it was given the answer in advance. That is the circular reasoning. Chalub shows that the 1/(2N) is an assumption imported from the Wright-Fisher model. It is not a result produced by the mathematics. It was never tested against, or derived from, an actual population.

This changes the shape of the Neutral theory critique even further in my favor, not that I needed any more ammunition given the N/Ne switcheroo as well as Frankham’s empirical evidence that conclusively demonstrates the inapplicability of Kimura to sexually reproducing populations. But until now, the defense of Kimura’s cancellation still had one retreat available: the critic does not understand the diffusion approximation, the result is a theorem of the continuum limit, the mathematics is above his pay grade.

That retreat is now closed, and it was eliminated by a mathematician who was not even paying any attention to its manifold implications for evolution, neutral theory, or the molecular clock. The initial-frequency theorem is not a theorem of the diffusion. The diffusion cannot produce it. The diffusion has to be provided as an assumed condition that is supplied in order to solve the equation. Which means that when we say Kimura’s cancellation rests on an assumption about how populations reproduce rather than on the math, we are no longer making an assertion that requires the reader to side with us against the scientific consensus on the basis of the evidence and the logic. We are simply repeating an observation that has been independently established in the mathematical literature and can be confirmed by anyone with the math to do so.

Kimura’s assumption about populations were always the whole of his argument. Chalub proved this, published it, and no one in population genetics appears to have either read it or realized its implications.

And if you don’t grasp what this means, here is the short version: every molecular single clock has been eliminated. All of the scientific estimates about deep time are gone. Even my own attempts at recalibration are hopeless, irrelevant, and wrong. It’s not just the CHLCA estimate. It’s also the Cambrian “molecular dates predate the fossils” literature, which was used for two decades to argue the fossil record was systematically incomplete rather than that the dates were all systematically wrong. This includes the mammalian radiation and whether it preceded or followed the K-Pg. The out-of-Africa chronology. Every single eukaryotic node in TimeTree. And the dating of angiosperm origins that produced Darwin’s abominable mystery in its modern form.

All of these time frames were calculated with a rate constant that has no derivation and no non-circular calibration. And all of it is now worthless and mathematically invalid nonsense.

For those who still have no idea what I’m talking about, let me make it even more straightforward for you. And rest assured there isn’t an evolutionary biologist on the planet who can even begin to successfully argue against any of this.

Dear Biologists,

Evolution is mathematically impossible. Your historical time estimates are not only wrong, they cannot even be calculated on the basis of the information you have. And your most important equations from your best guys are mathematically nonsensical.

Love,

The Mathematicians

DISCUSS ON SG


Skip the Sunscreen

Another lesson in what trusting the science and the media gets you:

The LARGEST sunscreen-skin cancer study EVER conducted found sunscreen users faced FAR higher risks of EVERY major skin cancer.

  • INVASIVE MELANOMA: +292%
  • MELANOMA IN SITU: +258%
  • BASAL CELL CARCINOMA: +140%
  • SQUAMOUS CELL CARCINOMA: +126%

Large increases in all major skin cancers can NOT be explained away by the “sunscreen paradox” given that lab tests found BENZENE, a known human CARCINOGEN, in 27% of sunscreen products tested.

Slathering rapidly absorbed carcinogens all over your body will unsurprisingly raise skin cancer risk.

At this point, your best bet is to ignore absolutely everything the media reports that The Science is recommending. Some of us can still remember when microwaving carbohydrates in margarine, then going outside covered in sunscreen was optimizing your health.

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The Revenge of Aaron Rodgers

Steelers’ fans have to love seeing this side of Aaron Rodgers come out on camera. The man hasn’t forgiven, he hasn’t forgotten, and he’s not afraid to take shots at Fauci aka “The Science”.

“Can you tell us about your mindset coming into your last season?” McAfee asked.

The ESPN personality couldn’t have guessed where the four-time NFL MVP was about to take this.

“I’m gonna plead the Fifth,” Rodgers said, as McAfee and the Steelers fans watching at training camp laughed in unison, “like that absolute coward Tony Fauci. Absolute coward. Are you kidding me? You get a pardon and you’re and you pleaded over 100 times at the White House? What are you scared of, Tony? I thought you were the science. ‘I am science,’ and you get up there and can’t answer a question? What are we doing?”

Rodgers is referencing an infamous remark by Fauci during a 2021 interview, where he defended his public health policies during the COVID-19 pandemic, saying “I am the science.”

Fauci pled the Fifth 111 times during a Senate hearing, on the advice of his attorneys, to answer questions from the Homeland Security and Governmental Affairs Committee, chaired by Sen. Rand Paul, R-Ky. He was questioned over his response to the pandemic and his policy decisions, where the former White House COVID advisor was accused of funding gain-of-function research in Wuhan, China.

Paul argued Fauci using the Fifth Amendment was unsupported because former President Joe Biden, whom Fauci advised during the pandemic, pardoned him. Paul said the committee will vote this week on a resolution to hold Fauci in contempt of Congress.

“Spending the entire time writing in his diary about how fun it is to be famous. You gotta be kidding me,” Rodgers’ diatribe continued. “What are you going to say about me now? Seriously, what are they going to say about me now? Now, you can’t talk about the COVID stuff because obviously it was made in a lab in China. That’s like not even questionable anymore. Tony Fauci is an absolute criminal. It’s been a great life and a great career, and it’s nice to be sitting on this side now especially after that coward pled the Fifth over 100 times. I thought you had a pardon there, Tony. Why couldn’t you answer one question the entire time?”

But Rodgers wasn’t just airing out his grievances with Fauci, which he’s had many since the days of the pandemic. He pointed the finger directly at the network he was being interviewed on. No matter how McAfee and his supporting cast tried to get the conversation back on track, Rodgers took aim at ESPN, the sports network giant which airs “The Pat McAfee Show” daily.

Rodgers was vilified for his own 2021 COVID saga, where his “immunized” remark in August, months prior to testing positive for COVID-19 in November of that season, led to immediate backlash in the media.

“How much time did the network spend on that? How much time did they spend on my responses every week? On my vaccination status? On Taylor’s [Swift] and Travis’ [Kelce] wedding? Did they do even a minute on Tony Fauci? No, I bet they didn’t,” Rodgers said on Monday.

Rodgers even brought up the Dianna Russini scandal and the lack of coverage from ESPN with it. Russini, who worked for ESPN before joining The Athletic, was entrenched in a scandal as an inappropriate relationship with New England Patriots head coach Mike Vrabel was exposed this offseason, ultimately leading to her exit from The Athletic and Vrabel leaving the Patriots during the NFL Draft to enter counseling.

There are a lot of people who would like to sweep their words and actions during the fake Covid crisis under the carpet. No one should permit them to do so. Rodgers clearly throws rhetorical bombs with the same degree of accuracy he throws the football.

DISCUSS ON SG


Another Failed Critique

The Kurgan discovers that AI critiques of Probability Zero cannot be relied upon.

In going over the summaries I did of your work and getting AI to check I have fairly represented what you say, I got the following, which I believe to be a narrative/adjacent/hallucination type of error AI is very prone to, as I have now become very familiar with. But I have not had time to review your latest updates, so I thought it best to get it direct from you.

The issue I am having is the text the AI spat out below when I basically said its objections didn’t make sense. It tried to say that not ALL models assume 100% replacement per generation as if that somehow invalidate your conclusions, (it had to concede almost all do use 100% replacement) So, I told it : “But in any case, even if a model does NOT assume 100% replacement that just makes Vox’s point even more valid.” This was its reply:

Does an incomplete-turnover model make Vox’s point more valid? No — and this is the part I have to be straight with you about, because it’s a vulnerability sitting in plain sight.

There are two claims tangled together:

1. Turnover is incomplete — generations overlap, not everyone is replaced each generation.
2. Incomplete turnover slows evolution, by something like a factor of d.

Claim 1 is trivially true and nobody disputes it — and yes, the Moran model “agrees.” But the entire force of the Selective Turnover Coefficient rests on Claim 2, and Claim 2 is precisely what the overlapping-generation models refute. Work evolution out in the Moran model — the rigorous overlapping-generations treatment — and you do not get a 1/dslowdown. You get essentially Wright–Fisher dynamics, and for drift, if anything, slightly faster. The unambiguous version: the substitution rate of neutral mutations equals the mutation rate whether or not generations overlap (Kimura’s result is model-independent), and a beneficial allele still fixes with probability ≈ 2s (Haldane). The engine runs at the same speed regardless of what fraction turns over per nominal “generation.”

What the d ≈ 0.45 factor actually is, is a units problem, not a biological brake. If only 45% of the population is replaced in what you’re calling a “generation,” then your “generation” is just shorter than the real mean generation time — you’ve relabelled the clock, not slowed it. The real biology (births, deaths, selection per year) is untouched, and mainstream models already handle it by using the actual generation time. There’s no hidden 1/d penalty waiting to be recovered.

So the strategic position: the moment you correctly narrow the claim to “Wright–Fisher,” you’ve conceded overlapping-generation models exist — and a population geneticist’s very next sentence is “right, and the Moran model does overlapping generations and produces no turnover slowdown, so where does the Bio-Cycle Fixation Model’s 1/d actually come from?” The burden then falls on Vox’s model to explain why it diverges from the standard overlapping-generation result, and I don’t think it can.

Error 1: “Kimura’s result is model-independent.”

This is false. The book addresses this explicitly. The derivation of k = μ goes:

k = 2N · μ · 1/(2N) = μ

The cancellation depends entirely on the initial-frequency theorem: the fixation probability of a new neutral allele equals its initial frequency, which is 1/(2N). And that result depends on exchangeability — every gene copy in the population must have the same probability of being the ancestor of the entire future population. In a Wright-Fisher model with discrete generations, exchangeability holds by construction. In a real sexual population with overlapping generations, it fails, because gene copies carried by a 20-year-old with forty years of reproduction ahead of her are not equivalent to gene copies carried by a 55-year-old with two years left. Their probabilities of fixation differ because their expected reproductive contributions differ. Once exchangeability fails, the fixation probability is no longer 1/(2N), the cancellation doesn’t go through, and k ≠ μ.

The critic asserts model-independence without engaging the assumption on which the derivation depends. That’s not a refutation. It’s a restatement of the claim being challenged.

Error 2: “The Moran model does overlapping generations and produces no turnover slowdown.”

The Moran model replaces one individual per time step — one birth, one death, chosen uniformly at random. It is “overlapping” in the trivial sense that not everyone dies at once. But it preserves exchangeability by construction: every individual is equally likely to be chosen for reproduction and equally likely to be chosen for death. There is no age structure, no differential reproductive value, no biological reality in which a grandmother and a teenager have different expected future contributions to the gene pool.

The whole point of the Selective Turnover Coefficient is that real overlapping generations are not Moran-style overlapping generations. In a real human population, individuals who were already adults in generation N are still reproducing in generation N+1, and they carry their existing allele frequencies forward, diluting the effect of selection on the new cohort. The Moran model abstracts this away by making every individual interchangeable at every time step. Citing it as evidence against d is citing a model that assumes exchangeability to refute an argument that exchangeability fails. That’s circular.

Furthermore, the Moran model isn’t the one that is relied upon by any biologists anyhow. The Moran model is a less-effective attempt to correct for the very Kimura-Wright-Fisher model that is the standard in population genetics.

Error 3: “d is just a units problem — you’ve relabeled the clock, not slowed it.”

This is wrong, and the book explains exactly why.

If you redefine the “generation” to be longer (so that 100% turnover occurs per redefined generation), you get fewer generations over the divergence interval. The math doesn’t change because d enters the calculation twice and in the same direction: it reduces both the effective selection per generation (Δp ≈ d · s · p(1-p)) and the number of effective generations (G_eff = G · d). You can’t escape this by rescaling one without rescaling the other. The total selective work done over the divergence period is d² times what the discrete model predicts, not d times, and no unit conversion eliminates a squared factor.

More importantly, the “units problem” claim is empirically falsified. If d were merely a relabeling, then the standard Kimura model and the Bio-Cycle model would produce the same predictions for allele frequency trajectories. They don’t. The book tests both models against three independent ancient DNA time series — LCT, SLC45A2, and TYR — using published selection coefficients. Kimura systematically overpredicts, driving alleles to near-fixation when observed frequencies are substantially lower. The Bio-Cycle model with d ≈ 0.45 for the Neolithic reduces prediction error by an average of 69% across all three loci. Three independent loci, different selection pressures, different time periods, different geographic regions, all converging on the same correction factor. A “units problem” doesn’t produce systematic overprediction in one model and accurate prediction in the corrected model. A real biological constraint does.

The AI critic’s objection follows a familiar pattern. It defends k = μ by citing models (Wright-Fisher, Moran) that assume the very thing being contested (exchangeability), calls the correction a relabeling rather than a physical constraint, and never engages with the empirical validation that distinguishes the two models. It is, in short, exactly the kind of narrative objection that sounds rigorous until you check whether it actually addresses the math — at which point you discover it doesn’t.

DISCUSS ON SG


The Problem is Vaccination

Dr. Robert Malone clearly doesn’t know his history of epidemiology.

President Trump just signed a new executive order to align the pediatric vaccine schedule with best practices from other developed countries.

At first glance, President Trump’s new Executive Order appears to be about childhood vaccines. It is not. It is about who governs public health in America. The Order represents an attempt to shift authority away from an insulated public health bureaucracy and back toward elected officials who are accountable to voters…

The administration is effectively saying that vaccine policy should not be dictated by a self-perpetuating network of advisory committees, professional associations, and pharmaceutical stakeholders operating behind closed doors. Instead, it argues that elected officials, accountable to voters, have the authority to establish policy objectives and direct agencies accordingly.

Whether courts ultimately agree remains to be seen. The legal challenges will continue. But the constitutional argument is clear: agencies exist to execute policy, not create it independently.

For decades, vaccine policy has been largely insulated from democratic accountability. ACIP recommendations automatically trigger insurance coverage requirements, Medicaid obligations, participation in the Vaccines for Children program, school mandate discussions, and physician practice standards. A relatively small group of experts has wielded extraordinary influence over national health policy.

The problem is not vaccination itself. The problem is regulatory capture.

Vaccines are among the most important public health tools ever developed. Smallpox eradication alone stands as one of humanity’s greatest achievements. Polio, measles, diphtheria, tetanus, and other diseases caused enormous suffering before effective vaccines became available.

Malone here is committing the same fallacy as Daniel Dennett, Immanuel Kant, David Ricardo, and a whole host of others who fail to understand that X is not, and can never be, Not-X.

In fact, the more we see these fallacious appeals to “smallpox eradication” the more dubious I become that the smallpox vaccine ever actually worked; one wonders if the whole story about Dr. Jenner and the cowpox will hold up if one looks at other changes in technology, and hand-washing practices, and sewage systems that are responsible for the huge decline in deaths from previous causes of mortality.

But we know that vaccines didn’t even put a dent in the reduction of the harm caused by “polio, measles, diphtheria, tetanus, and other diseases” because the order of historical events absolutely precludes that. The massive decline in deaths in the USA, in England and Wales, and everywhere else that historically kept track took place before the first vaccine was even invented. It’s not just a lie, it’s a retarded and obviously false one.

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The Irony of the 8s

People who believe the earth was created 6000 years ago, when it’s actually 4.5 billion years old, should also believe the width of North America is 8 yards. That is the scale of the error.
—Richard Dawkins

And 8 yards has to be wrong, because an evolutionary biologist like Richard Dawkins believes that the width of North America is 8 and 1/4 inches. That is the scale of the error committed by someone who believes in the evolution of Man and thinks that there was time for the evolution of 205,000,000 base pairs in the time that was sufficient for, ironically, 8.

It’s more than a little amusing to see how evolutionists are observably worse at science than young-earth creationists.

Read the 2nd edition of Probability Zero, the number one bestseller in Biology, Evolution, and Genetic Science if you want to see how comprehensively and conclusively that statement is backed up. The hardcover and paperback editions will be available soon.

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