Latest / UK Column Radio / Bayesian Reasoning Destroys Covid and DEI | Jerm Warfare

Transcript

Bayesian Reasoning Destroys Covid and DEI | Jerm Warfare · UK Column Radio

0:30
Speaker 1: My name is Jim. This is Germ Warfare, The Battle
0:33
Speaker 1: of Ideas, Norman Fenton, thank you for joining me in
0:36
Speaker 1: the trenches. We have a mutual friend, Nick Hutson.
0:41
Speaker 2: Yes.
0:43
Speaker 1: I'd like to argue though, that I have more claim
0:46
Speaker 1: to that friendship simply by virtue of the fact that
0:49
Speaker 1: he lives thirty five minutes away from me.
0:54
Speaker 2: My first new Nick during the covid era era, because
1:00
Speaker 2: he was one of the dissidents along with me. So,
1:03
Speaker 2: and we'd met in London three or four times as
1:08
Speaker 2: well as communicated kind of like reasonably regularly.
1:12
Speaker 1: I guess, have you been to outside of the pond? No?
1:16
Speaker 2: No, I haven't. I mean, it's rather difficult for me
1:20
Speaker 2: to travel because, let's travel long distances, because I've got
1:24
Speaker 2: my wife has dementia. She now lives in a care home.
1:27
Speaker 2: But I've been, like was a full time care up
1:31
Speaker 2: until a year ago when we moved to an home.
1:35
Speaker 2: And yeah, so that's kind of like constrained my movements.
1:38
Speaker 1: Really, I should have had you on my podcast a
1:41
Speaker 1: long time ago, and my apologies for taking this long
1:44
Speaker 1: to have you on. I mean, you one of the
1:47
Speaker 1: most outspoken sort of a level, shall we say, scientists
1:56
Speaker 1: in the UK since the COVID era.
1:59
Speaker 2: Yeah, I like to classify myself as a mathematician rather
2:02
Speaker 2: than a scientist because I focus on the data and
2:10
Speaker 2: logic of in case of COVID for example, so I
2:16
Speaker 2: don't comment on the medical aspects, but purely on the
2:22
Speaker 2: data outcomes. And in particular I use a particular probabilistic
2:30
Speaker 2: statistical technique called Bayesian reasoning to come up to look
2:37
Speaker 2: at the claims. It's all about the claims that were made,
2:41
Speaker 2: for example, about how deadly the virus was, how effective
2:46
Speaker 2: the various interventions were, including vaccine, and that approach requires
2:56
Speaker 2: a combination of prior judgments prior information which is then
3:02
Speaker 2: updated in the light of the new evidence that you're seeing.
3:05
Speaker 2: That's what the Bayesian approach does. It does it properly,
3:07
Speaker 2: unlike a lot of the classical statistical techniques, like I say,
3:11
Speaker 2: you know, it's just been looking at some of the
3:13
Speaker 2: truly awful papers, for example, that are still being written
3:18
Speaker 2: claiming that vaccine you know, is effective at cussing cardiovascular instance,
3:25
Speaker 2: which is contradicts the data that I've seen on that.
3:29
Speaker 2: And the reason they are able to claim this is
3:31
Speaker 2: because they're using ludicrous assumptions in their statistical methods.
3:36
Speaker 1: At the risk of becoming exceptionally complicated. What is the
3:43
Speaker 1: Bayesian method? Simply speaking, the.
3:49
Speaker 2: Bayesian method is that you like start with some a
3:53
Speaker 2: prior probability for an unknown event. So, for example, if
3:58
Speaker 2: I it's somebody tosses a coin which is claimed to
4:01
Speaker 2: be a fair coin, it's usually reasonable to have a
4:06
Speaker 2: prior belief that it's fifty percent likely to come up heads.
4:10
Speaker 2: But if you toss it, let's save ten times, and
4:14
Speaker 2: it comes up heads each time, then the Baysian by
4:20
Speaker 2: Baysian assumptions, you should actually adjust your belief to come
4:26
Speaker 2: to a conclusion that there's there's no longer just a
4:28
Speaker 2: fifty percent probability that that is a fair coin. Okay,
4:34
Speaker 2: but this has come up with a revised, revised probability.
4:37
Speaker 1: So okay, But now I mean a coin is a coin, right,
4:40
Speaker 1: it's got heads and tails?
4:42
Speaker 2: Oh no, Well, in mind, you don't know that that's
4:44
Speaker 2: the thing. That's the prior assumption, right, Okay, you don't know.
4:47
Speaker 2: For example, it might have been it might have been
4:49
Speaker 2: a double headed coin. If there was if there were
4:52
Speaker 2: nine heads and one towels, then that rules out the
4:54
Speaker 2: double headed coin hypothesis. What it wouldn't rule out is
4:58
Speaker 2: that it's a biased coin. That it's that it's loaded
5:00
Speaker 2: in such a way that it's much more likely to
5:02
Speaker 2: come up heads. You'd have to do quite a bit
5:05
Speaker 2: more tossing to come to a definitive cusure. You would
5:09
Speaker 2: expect that after one thousand tosses, if it was a
5:13
Speaker 2: fair coin, you'd be roughly five hundred heads and five
5:17
Speaker 2: hundred towels. So at that point, with great confidence from
5:21
Speaker 2: a Bayesian perspective, you'd come to the same conclusion as
5:24
Speaker 2: a classical statistical perspective. But if over one thousand, if
5:29
Speaker 2: over one thousand tosses you get say even if you've
5:34
Speaker 2: got say five hundred and fifty heads, you would actually
5:38
Speaker 2: be very confident that that was not an entirely fair coin.
5:43
Speaker 1: Right, Okay, So then if we replace the coin with
5:45
Speaker 1: an injection in the arm, yeah, what do we see?
5:52
Speaker 2: So you'd have some you'd have some prior belief in
5:56
Speaker 2: this case, based on previous data about the probability, for example,
6:01
Speaker 2: that that will have some particular outcome. And then you're
6:06
Speaker 2: looking you're then looking at you're trying to gather as
6:12
Speaker 2: much data as you can about the actual outcomes you observe,
6:17
Speaker 2: and that may lead you to revise your belief about
6:20
Speaker 2: the probability of the assumed outcome.
6:25
Speaker 1: So you wrote a book fighting Goliath in this instance,
6:28
Speaker 1: Who's Goliath and Who's David? Yeah, so that's that book
6:33
Speaker 1: up there. This I wrote McCaulay, Martin Neil about the
6:38
Speaker 1: whole COVID period. Goliath is the whole kind of like government,
6:45
Speaker 1: pharmaceutical media conglomerate which was basically pushing across the whole world.
6:53
Speaker 1: Essentially the narrative that COVID was.
6:58
Speaker 2: Not just a completely novel virus, which is something that
7:01
Speaker 2: we also kind of dispute how novel it actually was,
7:05
Speaker 2: but that it was a uniquely deadly virus that threatens
7:11
Speaker 2: the entire world, and that we could only recover, we
7:16
Speaker 2: could only get out of the virus by unprecedented government interventions,
7:21
Speaker 2: starting with, for example, lockdowns, unprecedented lockdowns and social distancing
7:29
Speaker 2: and all that nonsense, followed by the mandatory COVID vaccine.
7:38
Speaker 2: And that's what we challenged Goliath on all of those points,
7:44
Speaker 2: because from very early on, we were observing, again from
7:48
Speaker 2: the limited data that was available that was independent and
7:53
Speaker 2: was not biased. We saw fairly early on that the
7:58
Speaker 2: the case fatality rate for people who were let's say,
8:03
Speaker 2: assumed to have COVID, which is another issue that we
8:06
Speaker 2: uncovered uncovered to be problematic, was far lower than the
8:11
Speaker 2: rate that was claimed at the time, and that that
8:15
Speaker 2: has clearly been proven to be correct in hindsight, because
8:21
Speaker 2: initially it was they were ridiculing, for example, Donald Trump,
8:26
Speaker 2: who said that he disputed the initial figure they were
8:29
Speaker 2: originally quoting, remember over three percent case fatalityate, I think
8:33
Speaker 2: three point seven and Trump tried to ridicule that, and
8:36
Speaker 2: they ridicled Trump for question that figure. And right from
8:40
Speaker 2: the start our very first paper, which instantly was one
8:42
Speaker 2: of the few COVID papers that she actually got into
8:45
Speaker 2: a peer review journal before they started to close ranks
8:48
Speaker 2: against us, we were saying then that the case fatality
8:52
Speaker 2: rate wasn't really much different from from the flu right
8:57
Speaker 2: around about it was less. We were saying sort of two,
9:02
Speaker 2: and that it only affected it was only likely to
9:04
Speaker 2: affect elderly people anyway, and that with hindsight does appear
9:10
Speaker 2: to be the consensus something like the consensus figure. So
9:14
Speaker 2: the claims that made originally were completely order of magnitude wrong.
9:19
Speaker 1: Thanks to people like Ferguson and and.
9:24
Speaker 2: He has a lot, Yeah, they were so Ferguson was
9:30
Speaker 2: wrong in the predictions about what would how many deaths
9:33
Speaker 2: there would be if there were no massive interventions. So
9:38
Speaker 2: that was all this this weird and historically inaccurate mathematical modeling.
9:47
Speaker 2: And of course Drosten came up with the the PCR
9:50
Speaker 2: test to determine whether a person did have COVID. Of
9:53
Speaker 2: course that's something that again through our day various data analyses,
9:58
Speaker 2: we showed a yeah, very again very much a basin
10:01
Speaker 2: approach there, because with the basin approach we were able
10:04
Speaker 2: to characterize exactly what the kind of force positive rate
10:10
Speaker 2: was for the vaccine, and that was massively underestimated because
10:15
Speaker 2: people didn't understand Bayesian probability essentially, and there's quite a
10:19
Speaker 2: bit about that in the book. And then of course
10:22
Speaker 2: going on to the vaccine effectiveness, we debunked all of
10:26
Speaker 2: the claims about that.
10:28
Speaker 1: It's interesting, I don't know anything about mathematics. It wasn't
10:32
Speaker 1: one of my strong points at school. But what I
10:34
Speaker 1: can tell you is that my wife and I decided
10:37
Speaker 1: way back in twenty twenty that we were not going
10:40
Speaker 1: to get vaccinated. We weren't even going to get a
10:42
Speaker 1: PC artist, and we weren't going to legitimize the system.
10:45
Speaker 1: And if it meant that we would never be allowed
10:47
Speaker 1: to leave our country again, then so be it. As
10:51
Speaker 1: it happens, we can leave the country, and I think
10:55
Speaker 1: we are better for it not having that injection in
10:57
Speaker 1: the arm. But I'm thinking back to somebody like Nasim
11:01
Speaker 1: Talib who also kind of deals in risk analysis in maths,
11:07
Speaker 1: and he got it horribly wrong.
11:11
Speaker 2: Oh the guy, I mean, to be fair, I don't
11:13
Speaker 2: know the guy personally. I used to read his books
11:16
Speaker 2: and I thought that they are very interesting and engaging books,
11:20
Speaker 2: but he never actually gave any real solutions to the
11:25
Speaker 2: problems that he was claiming he could solve. And during COVID,
11:29
Speaker 2: ifat before COVID, he made some bizarre statements about the
11:34
Speaker 2: risk of climate change, which actually contradicted all of his
11:38
Speaker 2: previous beliefs because he was essentially sam, well, we don't know,
11:42
Speaker 2: we don't know what the real probability of the world
11:45
Speaker 2: ending because of man made climate change, but because it's
11:49
Speaker 2: such a potentially catastrophic risk, we have to basically again
11:54
Speaker 2: have these massive global interventions which changed the entire way
11:58
Speaker 2: of life of most people on Earth. So it completely
12:01
Speaker 2: contradicted if you read his earlier books, what he would
12:04
Speaker 2: say about risk. So he was talking complete nonsense about
12:11
Speaker 2: climate change on the basis that he just fell into
12:14
Speaker 2: the you know, the kind of like government approved narrative
12:19
Speaker 2: of catastrophic man made climate change which is going to
12:23
Speaker 2: end the world in not too many years. He just
12:26
Speaker 2: and if anybody believing that, you'll fine. Actually exactly the
12:30
Speaker 2: same people who believe that were the people who believed
12:34
Speaker 2: that COVID was going to potentially destroy the world if
12:40
Speaker 2: we didn't have again, these massive global interventions.
12:44
Speaker 1: So what peaked your interest. Let's go back to twenty nineteen,
12:48
Speaker 1: I mean, you were working in criminal trials. What got
12:50
Speaker 1: you curious about the COVID narrative?
12:54
Speaker 2: Ah, because from earlier than that, Okay, I was of
12:58
Speaker 2: course doing a lot of the stuff on for end,
13:01
Speaker 2: I was doing using the sort of Bayesian approach to
13:03
Speaker 2: do reasoning in forensic DNA analysis and stuff like that.
13:07
Speaker 2: So I used to do quite a lot of work,
13:08
Speaker 2: including expert witness work in the area. But actually for
13:12
Speaker 2: several years before that and during COVID, I was involved
13:17
Speaker 2: in a lot of research in medical risk and in
13:20
Speaker 2: particular risk of chronic diseases like room tied arthritis, diabetes,
13:26
Speaker 2: chronic heart failure, working with clinicians to do things like
13:31
Speaker 2: early early risk assessment, you know, how can we identify
13:36
Speaker 2: how likely it was for people with particular attributes that
13:39
Speaker 2: they were going to suffer from this, and what were
13:41
Speaker 2: the optimal treatment strategies. So we were applying Bayesian reasoning
13:46
Speaker 2: calls or modeling the sort stuff we do. These so
13:48
Speaker 2: called Bayesian networks we were building which are effectively like
13:52
Speaker 2: kind of that intelligent AI type models to do that
13:55
Speaker 2: type of reasoning and what if analysis, And we were
13:58
Speaker 2: working with clinicians were experts in the different areas of
14:03
Speaker 2: chronic diseases and using their expertise and a combination of
14:07
Speaker 2: data and trials and that type of thing. So I
14:12
Speaker 2: had a very large interline, it interdisciplinary project at the
14:17
Speaker 2: time COVID started in twenty twenty, which was already looking
14:23
Speaker 2: therefore at the risk of these chronic conditions. So it
14:27
Speaker 2: was inevitable that when COVID came along looking at the
14:33
Speaker 2: risks involved associated with that was very much in line
14:38
Speaker 2: with the work we were doing and indeed, I had
14:41
Speaker 2: a number of clinical colleagues who were able to provide
14:45
Speaker 2: the necessary expertise in the original modeling that we did.
14:48
Speaker 2: COVID was actually again before I was massively censored and canceled.
14:52
Speaker 2: I before I was challenging the official narrative in a
14:56
Speaker 2: way that they saw threatening, we were actually doing this
15:00
Speaker 2: kind of risk assessment. So we had a very sophisticated
15:03
Speaker 2: online We produced a very sophisticated online tool which was
15:08
Speaker 2: based on one of these basic network models underlying it,
15:11
Speaker 2: where people could simply enter enter basic information about themselves
15:17
Speaker 2: their health, and then update it with any new information
15:21
Speaker 2: like whether they had a particular test outcome, and they
15:24
Speaker 2: would get an updated probability of them likely to get
15:31
Speaker 2: different types of severity of COVID or would need hospitalization
15:35
Speaker 2: and stuff like that. That's what we did very early on.
15:38
Speaker 2: Of course, at that point that was very much kind
15:43
Speaker 2: of appreciated by the blog by Goliath, if you like.
15:46
Speaker 2: But that was about we were doing that in kind
15:49
Speaker 2: of like the spring of twenty twenty. But we were
15:55
Speaker 2: already seeing that the case fatality race, as I mentioned earlier,
15:59
Speaker 2: was this was just not a serious issue as people
16:02
Speaker 2: making out and actually fact, what we were predicting, you know,
16:05
Speaker 2: for people, the chances that people were going to be
16:07
Speaker 2: hospitalized and stuff like that, of course were coming out
16:10
Speaker 2: to be very really coming out to be very low,
16:13
Speaker 2: and of course that was not appreciated. And thereafter we
16:17
Speaker 2: discovered that even our predictions were wrong because we had
16:23
Speaker 2: assumed they were an overestimate at that time, because we
16:27
Speaker 2: had assumed that people who tested PCR positive, we assumed
16:31
Speaker 2: that was a true gold standard, and we assumed that
16:34
Speaker 2: was genuinely people who had this deadly novel virus, and
16:39
Speaker 2: of course that turned out to be completely wrong. And
16:42
Speaker 2: thereafter a lot of our work from then on was
16:46
Speaker 2: actually doing the adjustments to predictions about what was happening
16:52
Speaker 2: in terms of the trends, k COVID case rates, hospitalization rates,
16:55
Speaker 2: all that stuff. It was all it all had to
16:58
Speaker 2: be adjusted to take aunt of these false probability rates
17:03
Speaker 2: on these PCR tests. Basically, if you mass test a
17:08
Speaker 2: load of people, even if the so called even if
17:11
Speaker 2: the probability, the so called false probability rate, the probability
17:16
Speaker 2: that a person who doesn't have the disease test positive,
17:19
Speaker 2: even if that's quite low, let's say one or two percent.
17:23
Speaker 2: If you're testing millions of people, then actually you're going
17:28
Speaker 2: to get most of the people who actually are testing
17:32
Speaker 2: positive don't actually have the don't actually have COVID, and
17:37
Speaker 2: if you're testing, and what was happening is they were testing.
17:40
Speaker 2: They were massively ramping up the testing, which the effect
17:43
Speaker 2: of walls, which was inevitably to massively ramp up the
17:46
Speaker 2: number of claim cases, which was a complete art statistical
17:50
Speaker 2: artifact of the false probability rate. It just wasn't wasn't there.
17:54
Speaker 2: So but there was a reason they were doing this.
17:57
Speaker 2: They were doing it in the starting the autumn because
18:00
Speaker 2: in the summer of twenty twenty, basically COVID almost went away, right,
18:04
Speaker 2: it had really dropped, And so they stopped the lockdowns
18:07
Speaker 2: in the UK and plenty of other cases, and students
18:10
Speaker 2: were allowed to go back to school and to university,
18:13
Speaker 2: and people allowed to go back to certain jobs that
18:16
Speaker 2: most jobs in the autumn in September, but they could
18:20
Speaker 2: only do so if they were being tested PCR tested
18:23
Speaker 2: or lateral flow tested every single day. So you had
18:27
Speaker 2: this unbelievable number millions of people. It got up to
18:31
Speaker 2: over a million people a day were being tested. For example,
18:33
Speaker 2: in the UK, and the inevitable outcome for that was
18:36
Speaker 2: this observation of a massively increasing COVID rate, which incidentally
18:42
Speaker 2: was like three or four times. They were publicized four
18:46
Speaker 2: times the rates of COVID instance of COVID in the UK.
18:50
Speaker 2: Then at the peak, the supposed peak and the spring
18:52
Speaker 2: of twenty twenty, but it was all it was all
18:55
Speaker 2: because of this PCR testing. Yes, you have a slight
18:58
Speaker 2: increase because you comeing towards winter and you got the
19:02
Speaker 2: flu and everything else at that time, but there was
19:04
Speaker 2: no There wasn't this massive so called second peak, which
19:08
Speaker 2: they will claim, but they were claiming that second peak
19:10
Speaker 2: for a very specific reason. They needed that second peak
19:14
Speaker 2: to then say right, we're going to winter now unless
19:18
Speaker 2: we have it, and they were going to go into
19:19
Speaker 2: another lockdown then, and they did, and then they said, well,
19:22
Speaker 2: the only way out of this lockdown now is the vaccine,
19:26
Speaker 2: mandatory vaccine. And it was always about going to be
19:29
Speaker 2: about the vaccine at that point. This was pushed by
19:32
Speaker 2: the governments and the pharmacy companies.
19:35
Speaker 1: I remember Heah in South Africa. It was very much
19:38
Speaker 1: the same thing. But what didn't make sense to me
19:44
Speaker 1: is the foundational claim that this was a highly contagious,
19:49
Speaker 1: deadly novel virus. So here we have what we call townships.
19:54
Speaker 1: I suppose the international term would be what a shanty town.
19:58
Speaker 1: I suppose you have large clusters of very poor people
20:03
Speaker 1: living in very very close proximity, six people in a
20:08
Speaker 1: tiny little house and nothing was happening.
20:13
Speaker 2: We saw this. So there were so many places all
20:15
Speaker 2: over the world where adjacent areas one was claiming very
20:19
Speaker 2: high COVID rates and the other didn't have any instance.
20:21
Speaker 2: And of course, again much of this was driven by
20:25
Speaker 2: the particular local responses to the so called virus. So
20:30
Speaker 2: for example, you will see lots of COVID cases if
20:36
Speaker 2: you start doing lots of testing in particular areas, as
20:39
Speaker 2: I've already indicated, that's already going to make a difference.
20:41
Speaker 2: Just testing.
20:42
Speaker 1: Well, the same thing happened as the same thing happened
20:44
Speaker 1: with HIV AIDS. They also used PCR testing to inflate
20:48
Speaker 1: the numbers, yep.
20:51
Speaker 2: And also not just that, but also the death the
20:54
Speaker 2: hospitalizations and deaths actually is even worse, right again, all
20:58
Speaker 2: based on the localized responses to the virus. Of course,
21:03
Speaker 2: Sir Nick Hudson, as you know, you know, we wrote,
21:06
Speaker 2: you know, we did work with Nick Cardson as well
21:08
Speaker 2: on this, and these was very adamant about this, as
21:10
Speaker 2: are we that these unbelievably localized high death rates, for
21:15
Speaker 2: example in the spring of twenty twenty in places like
21:18
Speaker 2: Northern Italy, in London, in New York, for example, they
21:23
Speaker 2: weren't genuine COVID death clusters. They were genuine increases in
21:29
Speaker 2: deaths due to the inappropriate response to the virus. So,
21:33
Speaker 2: for example, we know for sure that many deaths were
21:38
Speaker 2: there were many unnecessary or earlier than should have been
21:42
Speaker 2: deaths of people in hospitals and care homes because they
21:45
Speaker 2: weren't getting the appropriate care that you need for a
21:50
Speaker 2: flu type virus or indeed any type. And many people
21:54
Speaker 2: were dying being classified as PCR COVID deaths, dying with
21:59
Speaker 2: you know, condition like cancer, heart disease, et cetera, who
22:02
Speaker 2: just were coincidentally testing with the stupid PC artists for COVID.
22:07
Speaker 2: But they were not only they were a lot of
22:10
Speaker 2: those people not only were dying and classified as COVID
22:14
Speaker 2: deaths wrongly, but in many cases at times they weren't
22:16
Speaker 2: getting the care because hospitals were prioritizing covid above all
22:21
Speaker 2: over other conditions, including life critical conditions, And then you
22:24
Speaker 2: had the ridiculous situation whereby relatively relatively healthy people you know,
22:31
Speaker 2: would go in and then they'd be put on respirators
22:34
Speaker 2: totally again, totally inappropriate. In many cases, you know, there's
22:39
Speaker 2: this respiration was because the staff were too scared to
22:42
Speaker 2: do anything else. They thought that was the way that
22:44
Speaker 2: they wouldn't get they wouldn't get infected themselves. And you
22:47
Speaker 2: had the crazy situation of people who, of course, people
22:52
Speaker 2: who get a virus, you know, like covid or flu
22:55
Speaker 2: or whatever, what in many cases will end up killing
23:00
Speaker 2: people is bacterial pneumonia as a result of that. But
23:03
Speaker 2: because covid wasn't, of course bacterial, they were in the UK,
23:08
Speaker 2: for example, they banned regular anti they banned COVID patients.
23:14
Speaker 2: They stopped giving COVID patients the normal antibiotics would have based,
23:19
Speaker 2: which were basically needed for the bacterial pneumonia, and people
23:22
Speaker 2: were dying because of that. So in the care homes,
23:25
Speaker 2: people were dying earlier than they should have done because
23:28
Speaker 2: they didn't have the family support. And I know from
23:30
Speaker 2: bitter experience how important that is for people in care homes.
23:35
Speaker 2: If the families are denying access to their loved ones
23:40
Speaker 2: in care homes, they're going to die early, simple as that.
23:44
Speaker 1: Before we go any further. Because of the nature of
23:46
Speaker 1: my show and the audience that I have, there's been
23:50
Speaker 1: very vigorous debate over the years around the actual existence
23:55
Speaker 1: of sarz CoV two.
23:57
Speaker 2: I certainly wouldn't stick my neck out and got the
24:00
Speaker 2: full distance with people who argue that, which includes my
24:04
Speaker 2: close colleague Martin new and includes Nick Hudson as well.
24:08
Speaker 2: I don't feel that there's sufficient data and I don't
24:13
Speaker 2: feel I have this sufficient medical expertise to make a
24:18
Speaker 2: judgment on that. What I what I would say is
24:21
Speaker 2: that on the based on the I think that then
24:26
Speaker 2: there what I do believe is that there was something
24:30
Speaker 2: slightly you know, there was a slightly novel grown virus,
24:33
Speaker 2: but we get these all the time, and therefore it
24:36
Speaker 2: was only it only became an issue because they decided
24:40
Speaker 2: that that was what they were going to test for.
24:44
Speaker 2: So in that sense, in that sense, you know, in
24:48
Speaker 2: that sense that it's nothing because actually, if you wanted
24:50
Speaker 2: to look for a novel virus, you can find one
24:52
Speaker 2: anytime you want. And if you want to blow that
24:55
Speaker 2: into a global pandemic, you could do it in the
24:58
Speaker 2: way they did it, and of course, the psychological campaign
25:02
Speaker 2: to frighten people, to scare people enormous, enormous negative medical
25:10
Speaker 2: implications of that.
25:12
Speaker 1: You focused quite a bit on OENUS vaccine data. What
25:17
Speaker 1: did you find.
25:21
Speaker 2: We found that the once you took account of the
25:26
Speaker 2: fact that the data was skewed by misclassifying vaccinated people
25:35
Speaker 2: with cod that boat that what they do. They misclassified
25:39
Speaker 2: people who would PCRE positive and therefore classified as a
25:43
Speaker 2: COVID case within three weeks of the vaccination, They classified
25:47
Speaker 2: those people as unvaccinated, which is of course completely ridiculous.
25:52
Speaker 2: Fact it's known that one of the most likely times
25:57
Speaker 2: you're going to get COVID is within the first few
26:01
Speaker 2: days or two or three weeks of a COVID vaccination.
26:06
Speaker 2: If you're classifying all of those people as vaccinated, so
26:10
Speaker 2: you're putting them in the group of truly unvaccinated people
26:14
Speaker 2: who happen to get who happen to be PCR tested
26:17
Speaker 2: positive for COVID, and you're comparing those when you're looking
26:21
Speaker 2: at vaccine effectiveness with the vaccinated people who get COVID.
26:27
Speaker 2: But you're excluding the vaccinated people to get COVID in
26:30
Speaker 2: three weeks. It is a statistical certainty that even if
26:34
Speaker 2: that vaccine was you know it was nothing was just
26:41
Speaker 2: you know, saline solution, it's guaranteed that you will see
26:45
Speaker 2: figures of ninety five percent effectiveness for the vaccine, which
26:50
Speaker 2: then tapes away after three months towards zero and Hay, prestone,
26:55
Speaker 2: now you need to get if you're using that as
26:57
Speaker 2: you're driving your data, the vaccine effecting innes say, presto,
27:01
Speaker 2: you need to now get a boost to our second shot.
27:03
Speaker 2: So it's all again it was a statistical illusion, and
27:07
Speaker 2: that when you actually look at the when you actually
27:11
Speaker 2: take out the when you make the consideration for those differences,
27:16
Speaker 2: what we were finding over time, once you took out
27:20
Speaker 2: the various other biases in the OS data, was that
27:23
Speaker 2: in every almost every different age category, the death rates,
27:30
Speaker 2: the all cause mortality rates for the COVID vaccinated were
27:35
Speaker 2: slightly higher than those for the unvaccinated. We're not talking
27:39
Speaker 2: about I've never claimed hundreds of thousands of millions or
27:42
Speaker 2: millions of extra deaths like some people have claimed. I
27:45
Speaker 2: don't claim that at all. But there was a certain
27:50
Speaker 2: there was certainly an observable increase in all cause mortality
27:55
Speaker 2: in the vaccinated and I think I did one report,
27:59
Speaker 2: I think based on the c of twenty twenty two
28:02
Speaker 2: to twenty twenty three data in the UK which I
28:06
Speaker 2: argued that there is a case for the COVID vaccines
28:11
Speaker 2: causing an extra something like twenty odd thousand deaths in
28:14
Speaker 2: the UK, and of course the only ones that are
28:18
Speaker 2: formally classified as COVID vaccine death so those where there
28:22
Speaker 2: was an autopsy and then a formal you know what
28:30
Speaker 2: was that formal exam wherever they were had to go
28:32
Speaker 2: through the court. And of course there's only been about,
28:35
Speaker 2: you know, a couple of hundred of those, but we
28:37
Speaker 2: know that in reality the number of deaths results in
28:40
Speaker 2: from the vaccine was almost certainly more. It's almost impossible
28:44
Speaker 2: to get a person who claims to have where the
28:50
Speaker 2: family claims that their relative has died from the co vaccine,
28:55
Speaker 2: it's almost impossible to get that classified as a COVID
28:57
Speaker 2: death because it's because they want to make it impossible
29:01
Speaker 2: to pay the mandatory compensation.
29:03
Speaker 1: It's the same thing with AIDS here in South Africa.
29:06
Speaker 1: I don't think I might be wrong on this, but
29:08
Speaker 1: there was a period in time where doctors were not
29:11
Speaker 1: allowed to say cause of death was aids.
29:18
Speaker 2: Yeah, I mean I think that the Robert F. Kennedy
29:22
Speaker 2: Junior book, this Fauci book, goes into detail on that.
29:26
Speaker 2: I mean it's quite shocking. Yeah. And the similarities, yes, yes,
29:31
Speaker 2: to what they did. I mean there's fun enough. Also,
29:33
Speaker 2: don't know if you've ever interviewed a friend of mine,
29:35
Speaker 2: American doctor who was effectively struck off because of his
29:39
Speaker 2: again challenging the official pharmaceutical narrative, guy called Randall Bop.
29:44
Speaker 2: He wrote a book about Zeca. He wrote a book
29:46
Speaker 2: about Zeca, and you can see they did exactly that. Again.
29:49
Speaker 2: The Pake called it overturning zekea the pandemic that never
29:52
Speaker 2: was exactly the same playbook, exactly the same playbook. They
29:57
Speaker 2: played it all out there before before COVID.
30:01
Speaker 1: Mormon come on the lines that said that the vaccine
30:06
Speaker 1: saved twenty million lives.
30:12
Speaker 2: I did. Yeah, that was one of many studies based
30:15
Speaker 2: on this bizarre, you know, mathematical modeling. I think I
30:18
Speaker 2: did a kind of like a satirical piece, which was
30:21
Speaker 2: actually not that satirical because it was in a sense
30:25
Speaker 2: similar to what they were doing. It's a bit like
30:26
Speaker 2: I did. I think I said something like the following.
30:28
Speaker 2: Imagine that you collect every day from say January in
30:35
Speaker 2: the UK. We're talking about UK, where the winters, our
30:40
Speaker 2: winters in you know, are in the January February, and
30:43
Speaker 2: summers are in July, August, cetera. So imagine that you
30:48
Speaker 2: go into the garden. I go into the garden every
30:50
Speaker 2: day between January and say early June, and I recall
30:58
Speaker 2: the temperature. Okay, so what you'll see, of course, you
31:01
Speaker 2: put this on the graph. You'll see the temperature as
31:04
Speaker 2: gradually is going up. Right, It's a steady increase in temperature,
31:08
Speaker 2: the daily temperature. And I think we'll hang a sec
31:14
Speaker 2: if this. If I want to now extrapolate what is
31:17
Speaker 2: going on here without my knowledge about what summers and winter,
31:22
Speaker 2: et cetera. At that point, what am I going to do?
31:25
Speaker 2: I'm going to extrapolate that by the end of the year,
31:27
Speaker 2: we're all going to basically die of heat because it's
31:30
Speaker 2: just going to keep going up, isn't it. That's what
31:31
Speaker 2: we're seeing. So I think we'll hang on sec. We've
31:33
Speaker 2: got to do something about this. I'm going to basically,
31:37
Speaker 2: take a fan and put it in the garden. Plug
31:40
Speaker 2: on a fan and put it in the garden and
31:42
Speaker 2: see what happens as a result of that. And what
31:45
Speaker 2: happens I start measuring the temperature after that, and it
31:47
Speaker 2: starts dropping, and hey, presto, the intervention of my fan
31:52
Speaker 2: in the garden has saved the world from dying of heat.
31:55
Speaker 2: That is essentially what they were doing. They were looking
31:58
Speaker 2: at these increasing, fabricated, increasing COVID numbers, then looking at
32:04
Speaker 2: the when the COVID intervention was and of course at
32:07
Speaker 2: that point they stopped doing all the PCR, the mass
32:10
Speaker 2: PCR testing, So irrespective of what the irrespective of what
32:15
Speaker 2: the vaccine was doing, you're going to see the COVID
32:18
Speaker 2: rates coming down. And that was it. That was basically,
32:21
Speaker 2: that's the basis for all of those ridiculous claims that
32:24
Speaker 2: you see in the Lancet and elsewhere of you know,
32:27
Speaker 2: twenty million lives et cetera saying because of the COVID vaccine.
32:30
Speaker 1: So you're talking about a miscategorization bias, which is a
32:34
Speaker 1: logical fallacy. I remember Jessica Hockett came onto my podcast
32:38
Speaker 1: a few months ago and she was looking at the
32:41
Speaker 1: New York hospital data. And something she found which I
32:46
Speaker 1: hadn't thought about back in the day was when they
32:50
Speaker 1: were bringing out the COVID mortality data, it was coming
32:54
Speaker 1: out pretty much in real time, and she argues that
32:58
Speaker 1: that's not possible because it takes weeks to do autopsison
33:02
Speaker 1: to know why people are dying, for example. So what
33:05
Speaker 1: they were doing is that they were creating models.
33:09
Speaker 2: Yep, yep. I've seen the work because I know Jessica
33:13
Speaker 2: and I've met her. She came to actually came to
33:15
Speaker 2: London for the book launch event for Fighting Alive, and
33:19
Speaker 2: there's a lot of I think that there's a lot
33:22
Speaker 2: of credibility in what she Indeed, others are claiming about
33:26
Speaker 2: the fact that there were there was fabricated death data
33:29
Speaker 2: in New York and.
33:30
Speaker 1: Not just New York worldwide.
33:33
Speaker 2: Yeah, I'm sure that there was similar. Yeah, similar scams
33:36
Speaker 2: polled elsewhere. No.
33:37
Speaker 1: But what I'm what I'm getting at is you're talking
33:39
Speaker 1: about modeling, and so much of the COVID event was
33:43
Speaker 1: based on modeling.
33:45
Speaker 2: Yeah, yeah, all of the original Yeah, I mean, yeah,
33:50
Speaker 2: all about our world in data stuff, remember, rather than
33:54
Speaker 2: where everybody was looking at, you know, everything, you were
33:56
Speaker 2: looking at every single country. It wasn't real data. It
33:59
Speaker 2: was not it was not you know, it was you know,
34:03
Speaker 2: ignoring whether the PCR test recro It wasn't based even
34:05
Speaker 2: on PCR tests. It was based purely on modeling. There
34:09
Speaker 2: was no reality in that data, and yet they were
34:14
Speaker 2: claiming it was real time. They were getting real time
34:15
Speaker 2: data from all these different areas in the world. It
34:18
Speaker 2: was not true. That's now known to be a fact.
34:22
Speaker 2: What was What's happening? And of course I have written
34:24
Speaker 2: we did right about this is that anybody who came up,
34:29
Speaker 2: who published, submitted, or even managed very rarely to publish
34:33
Speaker 2: an article in a peer review journal that actually did
34:38
Speaker 2: show that there was excess mortality from the vaccines, or
34:42
Speaker 2: who did even show that COVID wasn't as deadly a
34:45
Speaker 2: virus as was being claimed, anything that challenged the official narrative. Generally,
34:51
Speaker 2: anybody like ourselves, our group and others who were submitted
34:54
Speaker 2: in papers on that never got to the review stage
34:57
Speaker 2: after April twenty twenty, or if it got to review stage,
35:03
Speaker 2: it was automatically rejected by the editors. Very rare cases
35:08
Speaker 2: it might have been sent to review, and even the
35:10
Speaker 2: reviews were favorable. They would then make sure it couldn't
35:13
Speaker 2: be publish because they were bring us some other reviewer
35:15
Speaker 2: rejected it. But in the main they were simply they
35:18
Speaker 2: weren't even getting reviewed. We couldn't even get them into
35:22
Speaker 2: the preprint servers and stuff like that. We had to
35:24
Speaker 2: go to. The only one that would take it was
35:25
Speaker 2: a thing called research Gate. Now, in very rare cases,
35:29
Speaker 2: there was a very small number which managed to get through,
35:33
Speaker 2: and almost every one of those that managed to get
35:35
Speaker 2: through has since been there's been forced to a redaction
35:40
Speaker 2: on it for the most ridiculous reasons, whereas papers that
35:47
Speaker 2: for example, were publishing lance it with the kind of
35:49
Speaker 2: ridiculous claims that we've spoken about, and one in particular
35:52
Speaker 2: that we challenged, which was the biggest observational study claim
35:55
Speaker 2: in the fires of vaccine was ninety five percent effective.
35:58
Speaker 2: We submitted a complaint about that, just a a rebuttal letter,
36:03
Speaker 2: not even but you won't even ask him for redaction,
36:05
Speaker 2: just a rebuttal letter, and they wouldn't accept that, and
36:08
Speaker 2: it took them eighteen months to reply to it. And
36:11
Speaker 2: in the end it was almost like a bit of
36:13
Speaker 2: a scandal because when I posted a tweet about their
36:18
Speaker 2: response after eighteen months, which incidentally said the reason they
36:21
Speaker 2: couldn't publish our letter was because the lead author of
36:25
Speaker 2: the paper we were criticizing had not responded to it.
36:29
Speaker 2: So the lack of response was a sufficient reason not
36:33
Speaker 2: to publish our letter pointing out the errors in the paper.
36:38
Speaker 2: And then there was a big reaction. There was a
36:39
Speaker 2: big reaction there invited us to resubmit the letter, and
36:43
Speaker 2: then two days later they rejected it again, and I
36:47
Speaker 2: got a freedom information I did a subject access request
36:51
Speaker 2: to get all of the correspondence email corresponds about that
36:56
Speaker 2: about my letter, and it turned out if you look
36:58
Speaker 2: at it again in the actually I think I've given
37:01
Speaker 2: that example, there's something about it. In both of those books,
37:04
Speaker 2: you'll find that it was. It wasn't scientific reasons they
37:07
Speaker 2: were rejecting my letter. It's because they were classifying me
37:10
Speaker 2: as conspiracy theorists and anti VAXXA. Therefore they couldn't have
37:14
Speaker 2: my name on a letter which was criticizing one of
37:18
Speaker 2: these ludicrous papers.
37:19
Speaker 1: It's quite clear to me that peer review itself has
37:22
Speaker 1: become ideological and utterly broken.
37:25
Speaker 2: Yeah, and that indeed, that's of course one of the
37:28
Speaker 2: subjects of my new book, and of course, of course
37:35
Speaker 2: the COVID my experiences during COVID feed into this book,
37:39
Speaker 2: because this book is actually about my forty seven years
37:42
Speaker 2: in academia and how I was eventually forced out in
37:45
Speaker 2: two thousand and twenty two basically because of my COVID activities,
37:53
Speaker 2: because I was challenging that official narrative. So I've got yeah,
38:00
Speaker 2: because I was challenging official cod COVID narrative, I went
38:04
Speaker 2: from kind of respected academic professor to an academic prior
38:10
Speaker 2: in a matter matter of just a few months. So
38:16
Speaker 2: I was rially you know, I was, you know, for example,
38:18
Speaker 2: even leading I mentioned about, I was leading that big
38:21
Speaker 2: intition in for research projects, you know, my medical even
38:24
Speaker 2: some of my medical colleagues refused to be named on
38:28
Speaker 2: papers where I was a co author, and I got
38:30
Speaker 2: thrown off of grant applications because they didn't want me
38:34
Speaker 2: on it. My lectures got canceled, not just at my
38:39
Speaker 2: host institution, Queen Mary, but external lectures, including including lectures
38:45
Speaker 2: which were nothing about COVID. In fact, you know, one
38:48
Speaker 2: major example, big NHS conference was actually going to be
38:51
Speaker 2: talking about these the work we were doing in the
38:54
Speaker 2: in risk assessment in those chronic conditions using these sort
38:57
Speaker 2: of AI models that got canceled simply because they said
39:02
Speaker 2: that they decided at the end my presence would be
39:05
Speaker 2: a distraction, and then they justified it later because the
39:09
Speaker 2: Free Speech Union took on the case and then they
39:11
Speaker 2: basically said, you know, no, They actually even accused me
39:16
Speaker 2: of being a kind of like Hitler sympathizer, which is
39:19
Speaker 2: bizarre because I'm Jewish, very publicly Jewish. My father was
39:23
Speaker 2: the only survivor of his family, large family in the Holocaust.
39:28
Speaker 2: They accused me of being a Hitler supporter because I
39:30
Speaker 2: used the the clown world symbol on some posts, and
39:35
Speaker 2: they said that clown world symbol is uh is basically
39:39
Speaker 2: a dog whistle for Hyle Hitler. There you go. That
39:43
Speaker 2: was that was the the NHS. That was one of
39:45
Speaker 2: the justifications the NHS used for And incidentally, that was
39:50
Speaker 2: it was a kind of a posterior justification because they said,
39:54
Speaker 2: you know, after after they canceled me, then they discovered
39:58
Speaker 2: they were rights of cancer me because of these these
40:00
Speaker 2: kinds of posts. So you've got that all that nonsense.
40:02
Speaker 2: So basically this happened, right, but it was going this
40:07
Speaker 2: way anyway, not just it wasn't just COVID. In fact,
40:10
Speaker 2: this you know that this new book, there's only just
40:13
Speaker 2: you know, of course it talks about my experience during
40:16
Speaker 2: COVID at the end, because that's what ultimately led to
40:19
Speaker 2: my to me being effectively forced out of academia because
40:24
Speaker 2: there was no obviously no future for me there. But
40:27
Speaker 2: I wrote this big book because a lot of the
40:31
Speaker 2: a lot of because I regard academia certainly as being
40:36
Speaker 2: responsible for a lot of the major problems during COVID.
40:39
Speaker 2: It was it was academics who drove the ludicrous modeling.
40:42
Speaker 2: It was academics who were driving the nonsense on the
40:46
Speaker 2: testing and the claims of vaccine effective and all of
40:49
Speaker 2: that stuff. It was academics, you know, who were advising
40:53
Speaker 2: the government to lock down right. You even had in
40:56
Speaker 2: the UK on the Government's Stage Committee, which committee which
41:00
Speaker 2: was basically set up to as the task force for
41:05
Speaker 2: you know, how to respond to COVID. The person in
41:07
Speaker 2: charge of the behavioral insights on that it was a
41:11
Speaker 2: self declared communist professor called Susan Mickey who was who
41:16
Speaker 2: was openly boasting about the need to exaggerate, we need
41:20
Speaker 2: to use these nudge techniques, behavioral techniques to exaggerate, to
41:26
Speaker 2: create much greater fear than was than you know, than
41:30
Speaker 2: was maybe necessary. They wanted to create the fear because
41:32
Speaker 2: otherwise they weren't going to force people into lockdowns and
41:35
Speaker 2: stuff like that. So it was academics who were behind it.
41:39
Speaker 2: And it was academics also, I believe, and this is
41:42
Speaker 2: another recurring feature in the book, behind this whole climate
41:48
Speaker 2: man made climate catastrophe narrative as well. And generally what
41:54
Speaker 2: the book talked about is the fact that you know,
41:57
Speaker 2: the universities no understanding. Want to get clear, people have
42:02
Speaker 2: no runs people outside of universe. They have no understanding
42:04
Speaker 2: of how awful they are and how different they are
42:07
Speaker 2: to what people perceive them to be. So they don't
42:11
Speaker 2: It's not just a question of them educating the elites
42:15
Speaker 2: who shape I mean they basically they do educate. This
42:19
Speaker 2: is the thing. They do educate the elites, and by elites,
42:22
Speaker 2: I mean everybody who then goes into the institutions, the
42:25
Speaker 2: people who shape the assumption, values, ideologies that govern public life,
42:29
Speaker 2: and they've all basically been indoctrinated with the dominant ideology
42:34
Speaker 2: that's been in universities for many years, going back decades,
42:38
Speaker 2: and that ideology is increasingly based around cultural Marxism, globalism,
42:45
Speaker 2: critical race theory, and identity politics. And it's all about
42:49
Speaker 2: interpreting society through the lens of power, oppression, and group identity.
42:55
Speaker 2: And so you get each cohort of indoctrinated graduates come
43:00
Speaker 2: the politicians, the civil servants, the lawyers, the police, you know,
43:05
Speaker 2: the journalists, the media stars, and the regulators of tomorrow.
43:08
Speaker 2: And that's what that's what this book is about. And
43:11
Speaker 2: it's actually also about how essentially how I mean corrupt,
43:16
Speaker 2: but how overrated and ludicrous much of academia is. And
43:22
Speaker 2: the other recurring theme in it, and which of course
43:24
Speaker 2: has driven a lot of that ideology and identity politics
43:28
Speaker 2: that I mentioned is this whole DEI diversity, equity and
43:32
Speaker 2: inclusion infrastructure, which is although people know about it, not
43:36
Speaker 2: just about in universities. And incidentally, of course, I mean
43:41
Speaker 2: Trump's tried to do something about it in the States,
43:43
Speaker 2: but from a UK perspective, it has become dominant throughout
43:49
Speaker 2: our society, but it's especially dominant in the universities, which
43:53
Speaker 2: is why incidentally, now the book, at least the first
43:57
Speaker 2: print run of the book was written just before and
44:00
Speaker 2: I don't you know about this Jason R. Day case
44:02
Speaker 2: at Cambridge University. So it's a big, big story. It
44:05
Speaker 2: made it into its international news. Basically, there's this guy
44:09
Speaker 2: called Jason R. Day who was the youngest black professor
44:15
Speaker 2: ever appointed at Cambridge University in twenty twenty three. But
44:20
Speaker 2: he had this bizarre backstory that he couldn't speak until
44:26
Speaker 2: he was eleven, that he couldn't read and write until
44:28
Speaker 2: he was eighteen, that he had multiple brain tumors, that
44:34
Speaker 2: he nevertheless had performed these unbelievable physical feats like thirty
44:40
Speaker 2: marathons in thirty days, one of which he'd broken his
44:45
Speaker 2: leg and has swollen twice the size. And he then
44:50
Speaker 2: somehow from that managed to do a PhD, which is
44:55
Speaker 2: subsequently the allegations, I mean they are allegation. Advise you
45:00
Speaker 2: to look at the multiple reports on this. He basically
45:04
Speaker 2: plagiarized it from an MSc thesis, massive great chunks of it,
45:10
Speaker 2: and then subsequently got some really weird academic posts before
45:18
Speaker 2: very very rapid rise in a couple of years to
45:20
Speaker 2: first he got some he suddenly became a professor at
45:22
Speaker 2: Glasgow on the basis of almost no academic credibility whatsoever.
45:27
Speaker 2: And then sort of a year later he was appointed
45:31
Speaker 2: professor at Cambridge. Because what had happened in the meantime
45:34
Speaker 2: is he became a very public figure because of this
45:36
Speaker 2: bizarre backstory of his that, you know, from someone who
45:40
Speaker 2: couldn't read and write until the age of eighteen, managed
45:44
Speaker 2: to get a PhD, perform all these incredible, unbelievable feats
45:49
Speaker 2: and physical feats and made you know, he became a
45:54
Speaker 2: it was sort of a media figure, and so Cambridge
45:57
Speaker 2: and their unbelievable desire, their virtuous because Cambridge. There's a
46:01
Speaker 2: section of the book which was written, of course, I'll
46:03
Speaker 2: say before this story broke about the peculiar the special
46:07
Speaker 2: peculiarities of the dad I framework at Cambridge. You can
46:11
Speaker 2: kind of understand why they would want to get you know,
46:14
Speaker 2: this is the ultimate virtual sigma to grab this guy
46:18
Speaker 2: having this sort of the you know, the face of
46:22
Speaker 2: their diversity, equality and inclusion at Cambridge. But to say
46:27
Speaker 2: that this guy, to say that this guy was over
46:31
Speaker 2: promoted is to say that's an understatement. I mean, it's
46:34
Speaker 2: a bit like putting mister Bean in charge of air
46:38
Speaker 2: traffic control. It's a bit like putting Obama in charge
46:42
Speaker 2: of the United States. Exactly, yeah, exactly, But I think
46:46
Speaker 2: this is even more exaggerated than that. So it's a
46:48
Speaker 2: really really bizarre case. But this book explains exactly why.
46:53
Speaker 2: I mean, there's a section I mean just actually I
46:55
Speaker 2: want to just read this out because this is really
46:57
Speaker 2: this is really quite critical. Abridge University has this so
47:03
Speaker 2: called Behavioral Attributes Framework which it applies to every single
47:09
Speaker 2: person who applies for any post at Cambridge or any
47:12
Speaker 2: to any appointment or any promotion. So this is for
47:16
Speaker 2: all staff internally and externally. Okay, it's called the say
47:21
Speaker 2: the behavior actually its framework, and it's got a number
47:23
Speaker 2: of different attributes which people are judged on, have to
47:27
Speaker 2: be judged on in their applications, and they have to
47:30
Speaker 2: make their people have when they apply. I have to
47:32
Speaker 2: explain where how they how they fit with the necessary attributes.
47:36
Speaker 2: So you'll have obviously reasonable attributes like I don't know,
47:40
Speaker 2: scholarship or ever, but that will have different levels ABC,
47:43
Speaker 2: D or E depending on whether you're applying for a
47:46
Speaker 2: professorship or to be a cleaner. Right, so you've got
47:48
Speaker 2: different levels. Okay, But there is one attribute which is
47:53
Speaker 2: called valuing diversity, which only has a single level, and
47:59
Speaker 2: that's a level A. Every single person has to explain
48:03
Speaker 2: in their applications or including their promotion applications, how they
48:09
Speaker 2: satisfy the value diversity attribute. And I can tell you
48:13
Speaker 2: what they have to do for this read out things
48:16
Speaker 2: it includes. They have to show how they will promote
48:20
Speaker 2: an inclusive environment. Next one is even quite chilling. You
48:26
Speaker 2: have to challenge or report attitudes. You have to show
48:30
Speaker 2: that you're going to You have to confirm that you
48:32
Speaker 2: will challenge or report attitudes deemed inappropriate. In other words,
48:36
Speaker 2: you've got a snitch on people with whom you disagree
48:40
Speaker 2: and they will decide who you can disagree with, and
48:43
Speaker 2: generally that will not Yeah, we know the kind of people.
48:47
Speaker 1: Obedience to the state.
48:49
Speaker 2: The state and the final one affirm a broad range
48:52
Speaker 2: of cultural outcomes, beliefs and values. So basically you have
48:56
Speaker 2: got to show this tremendous ideological commitment to the whole
49:01
Speaker 2: d I nonsense, otherwise you won't get a job. And
49:05
Speaker 2: when you see that, you can see exactly how someone
49:08
Speaker 2: like Jason Arday got appointed as the youngest black professor
49:13
Speaker 2: at Cambridge University.
49:15
Speaker 1: It does make you question, though, where this stuff comes from.
49:20
Speaker 2: Oh, it's the book. The book instantly also covers this
49:24
Speaker 2: right because it actually started in the previous It started
49:28
Speaker 2: in the twentieth century, like I think it started eighteen
49:31
Speaker 2: ninety four with the formation in the UK of the
49:35
Speaker 2: Fabian movement, because they they knew that there wasn't going
49:39
Speaker 2: to be in the UK. There wasn't that you weren't
49:41
Speaker 2: going to be able to get a violent, a violent
49:43
Speaker 2: communist revolution that you know that you had taken place
49:47
Speaker 2: in other parts of Europe, France, and would take place
49:49
Speaker 2: in Russia and China later on because the culture, because
49:54
Speaker 2: there was too much sort of civil infrastructure, too much
49:59
Speaker 2: cultural cohesion for that to happen in a country like UK.
50:03
Speaker 2: So these Marxists, well call these the Fabians, but they
50:07
Speaker 2: are effectively socialists. They looking for a new world order,
50:10
Speaker 2: people like George Bernard Shaw is one of the first ones.
50:13
Speaker 2: They decided that the way they would achieve this new
50:16
Speaker 2: world order would be through the gradual infiltration of the institutions.
50:22
Speaker 2: And that's why they're symbol if you look it up,
50:25
Speaker 2: is the sheep in wolf's clothing. You're going to gradually infiltrate.
50:30
Speaker 2: And they decided that the university's education was where you
50:35
Speaker 2: have to achieve this infiltration first, because once you're there,
50:38
Speaker 2: you'll then get it into the civil service and then
50:40
Speaker 2: all of the other institutions of state. And in fact
50:43
Speaker 2: they actually set up one of the best, in fact,
50:46
Speaker 2: my old school, i'd say my old University college, London
50:49
Speaker 2: School of Economics, they actually set it out. The Fabians
50:52
Speaker 2: set that up in nineteen oh four specifically to promote
50:56
Speaker 2: this worldview. And they also in some incidentally were instrumental
51:01
Speaker 2: in the Education Act also early nineteen hundreds in the UK,
51:07
Speaker 2: which basically made the state responsible for most education. And
51:13
Speaker 2: so there was this gradual infiltration that was happening all
51:17
Speaker 2: the time. Now. In the nineteen twenties nineteen thirties, the
51:21
Speaker 2: so called Frankfurt School, which ironically was a bunch of
51:25
Speaker 2: far left academics who fled Nazi Germany. They went to
51:30
Speaker 2: the US and they decided to infiltrate all of the
51:34
Speaker 2: universities there, and they were very much focused on not
51:38
Speaker 2: also this gradual infiltration, but looking very much at the
51:41
Speaker 2: cultural aspects. They decided you had to capture the culture.
51:44
Speaker 2: So it wasn't just it wasn't just what you taught
51:48
Speaker 2: in terms of Marxist ideology. What you had to basically
51:54
Speaker 2: do was managed to get hold of media or media films,
52:00
Speaker 2: everything the written word, and that that would then reinforce
52:05
Speaker 2: itself within academia as well. So it was all about
52:08
Speaker 2: that cultural Marxism, and that's really what drove this whole
52:12
Speaker 2: dei stuff, you know, which was formerly kind of only
52:16
Speaker 2: came about sort of twenty years ago. But the basis
52:19
Speaker 2: for it, which was just the whole critical race series stuff,
52:22
Speaker 2: it was always It was there from the Frankfurt school,
52:25
Speaker 2: from the Fabians. It was well established in the mid
52:29
Speaker 2: nineteen sixties in American universities and it quickly then became
52:34
Speaker 2: you know, the critical racery stuff hadn't been a feature
52:37
Speaker 2: of British universities. The cultural the kind of the Marxism was,
52:41
Speaker 2: but the critical race I mean, in fact, a lot
52:43
Speaker 2: of those early Marxists were real, genuine racists. They hated
52:46
Speaker 2: black people, right, So that didn't infiltrate our universities. The
52:51
Speaker 2: critical racery stuff didn't infiltrate the UK universes until it
52:55
Speaker 2: had already infiltrated. We basically got it from the Americans
52:59
Speaker 2: and it's sort of a steady, increasingly rapid takeover of
53:04
Speaker 2: the institutions since then.
53:06
Speaker 1: But isn't it weird though, that the institutions allow it.
53:13
Speaker 2: The institutions allow it because the people who control the
53:16
Speaker 2: institutions have come through the same indoctrination the vice chancellors.
53:21
Speaker 2: You won't find apart from the single private university in
53:25
Speaker 2: the UK, every vice chancellor buys into this ideology. And
53:29
Speaker 2: again the book talks about that gives examples. You know
53:34
Speaker 2: my university that was twenty two years at last twenty
53:38
Speaker 2: two years. If you look at his mission statement. Actually,
53:41
Speaker 2: I want to get this right. I've misquoted the book
53:43
Speaker 2: myself in this I did say something like their mission
53:46
Speaker 2: statement above it. Let me just check this. I want
53:49
Speaker 2: to get this right. It's quite quite incredible. We're talking
53:55
Speaker 2: about yeah, the yeah. Here it is. I actually got
53:59
Speaker 2: the quote. This is the accurate quote, so Queen University
54:03
Speaker 2: in its in its mission statement. So this is the
54:05
Speaker 2: mission twenty thirty statement. This is what is on their
54:08
Speaker 2: the front page of their website. It's in bold letters
54:10
Speaker 2: on the front of their mission statement. It says our
54:13
Speaker 2: mission is to be the most inclusive university of its
54:17
Speaker 2: kind anywhere. Nothing about being the best in terms of
54:20
Speaker 2: some scholarship scholar you know, scholarship outcome or research outcomes.
54:24
Speaker 2: No to be the most inclusive. What the hell? Why,
54:28
Speaker 2: why the hell would that be the mission statement of
54:31
Speaker 2: any university.
54:32
Speaker 1: And for that matter, what is wrong with exclusivity?
54:38
Speaker 2: You'd think that exactly Universities were always the top universities
54:43
Speaker 2: were always highly highly exclusive, because that's where you get
54:47
Speaker 2: real meritocracy. You only get you are only going for
54:50
Speaker 2: the best and most gifted. And that has been blown
54:53
Speaker 2: out of the window by the idea that there should
54:55
Speaker 2: be universal higher eduate university education for everybody. Most of
55:00
Speaker 2: it's junk. Even a lot of the hard sciences has
55:03
Speaker 2: become junk because of the capture of the journals and
55:07
Speaker 2: the awful state of peer reviewing as well. I mean,
55:12
Speaker 2: as Brett Weyerson said, I did an interview with him
55:14
Speaker 2: in during COVID and we touched on this issue, and
55:18
Speaker 2: he said, academa is like a dog with Raby says,
55:21
Speaker 2: there's no saving it. It's time to get a new dog.
55:23
Speaker 2: And that's essentially the message of my book, And the
55:27
Speaker 2: final chapter of the book explains how we might achieve that.
55:30
Speaker 2: You've got to get rid of the system has to
55:32
Speaker 2: be overturned, and my argument for doing that is to
55:34
Speaker 2: basically defund it. Just you know, if you rely only
55:37
Speaker 2: on you should rely only on student fees. So it's
55:41
Speaker 2: actually a demand driven system. And people who only pay for,
55:47
Speaker 2: you know, for quality courses that they actually need to improve,
55:51
Speaker 2: you know, to improve lives and you know, improve the
55:54
Speaker 2: state of the nation. All of the junk DEI stuff
55:57
Speaker 2: and all of the junk courses and all of the
55:59
Speaker 2: nonsense age old stuff wouldn't be able to survive without
56:02
Speaker 2: the government grant.
56:04
Speaker 1: Well, Norman, I'm very pleased to say that I am
56:07
Speaker 1: a varsity dropout.
56:11
Speaker 2: Well done, and I need to Yeah, I'm pleased have
56:15
Speaker 2: had the interview because I need to go and visit my.
56:18
Speaker 1: Visit my wife. I know you your time is tired quickly.
56:20
Speaker 1: How can I follow your work and get your books?
56:25
Speaker 2: So the easiest thing is to just look go for
56:28
Speaker 2: if you look at if you Norman Fenton dot Com
56:31
Speaker 2: gets to my website and that's got information about the books,
56:35
Speaker 2: et cetera. If you want these books, just go on
56:38
Speaker 2: Amazon and google Norman Fenton books and you'll you'll get
56:41
Speaker 2: to them, all
56:42
Speaker 1: Right, Norman Fenton, Thank you for joining me in the trenches.