마르크스도 정의하지 못한 계급이 과연 무엇인지 저들은 말해줄 수 있을까? 공영 방송에서 공공연히 계급을 배반하지 말라며 선동하고 있다. 기생충다운 짓거리다.

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    중국 공산당의 선거 개입 방법 / 출처 파이낸셜 투데이

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산업화에힘써라

 

손석희가 엔번방 관여한거는 아닌듯 하고 우파 전향한 김웅 사기친 듯함..

존나 손석희가 엮였나 했는데 손석희 뺑소니라고 영상 팔아서 우파 전향한 김웅 천오백 뒷통수 친거 보니

골수 좌파 대깨문 맞네 ㅋㅋㅋㅋ / 일베  댓글

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테러 당한 문갑식 티비
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세월호 포스터 3탄.

자식 잃었다고 난리를 치더니, 참 대단한 사람들이다.

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위기가 발생하면 정부 기구의 권력이 더 강력해진다. 그리고 위기가 끝나도 그 권력은 사라지지 않고 계속 시민들의 자유를 억압하게 된다.

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네번의 총선, 여론조사 다 틀렸다

 

여론조사 다수서 민주당 우세 점쳐… 야권선 "숨은 야당표 있을 것"

고민정 43% 오세훈 32% 나온 조사, 응답자 62%가 민주·정의당지지 / 조선일보

--->여론조사가 아니라 여론 조작이다.

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정규재

황교안인 반문연대라는 미명아래
짬뽕공천(안철수계, 유승민계 김형동, 문병호 좌파까지도 공천) 함.

그 결과
첫째 당을 버린자가 당을 차지했고
둘째, 중도와 중도좌파가 당을 차지했으며
세째 문재인과 싸우지 않는 사람들이 당을 장악했다.

이건 순전히 황교안이 끌어들인 김형오
김형오가 끌어들인 김세연, 이석연이 다 말아먹었다.

황교안의 지도력은
누구라도 무능하고 의사결정 무능력이다라는 것을 다 인정할 정도다

이대로 가면 선거가 불가능하다!
그러나 해결할 방법이 없는 것은 아니다

첫째 지금이라도 최소 김형오가 쑥대밭으로 만들어 놓은 10군데 정도는 경선을 통해서라도 공천 조정이 필요하다
특히 대구경북, 부산등 절대우위 지역은 공천을 반드시 조정해야 한다.
둘째 황교안 대표는 지금이라도 당대표 자리 내려오고 지역구에 전념하고
선대본부장 박형준, 신세돈은 우파국민에게 감동을 주지못하니 선대위원장을 교체해라.
당은 보수우파 이념이 확고한 비상대책위원장으로 하여 비상체제로 가야한다

국난에는(코로나 사태)에는 지도자의 지지율이 올라간다.(벌써 그런 현상이 보인다)
여기에 문재인이 북한, 중국하고 한두번만 쑈하면 끝난다

이제라도 국민을 감동시킬수 있는 미래 비젼이 있어야 한다
황교안대표 머리로는 미래비전이 나올 수없다.

국민들의 마음을 뜨겁게 움직일 수 있는 미래비전이 나와야 한다 
자유의 원칙하에 어떻게 풍요로운 대한민국을 만들것인지
어떻게 좌경화된 지난 30년의 87체재의 관뚜껑에 못질을 할 건지
어떻게 사이비민주주의 오류를 바로 잡을 것인지
그 사이비민주주의 오류중에 가장 심각한게 탄핵이다
그것 다 모른척하고 뭐를 개혁한다는 것인가

황교안 대표는 지역선거운동에 몰두하고 자유우파세계관이 투철한  비상대책위원장을 모시고 대표자리를 내려놔라
적어도 pk10자리 김형오가 쑥대밭으로 만들어 놓은 10자리 정도는 되돌리지 않으면 안된다
정말 호소한다 정말 호소한다.  / 일베에 정리된 정규재의 말

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우한 폐렴 회복한 부산대 교수 증언

 

1. 초기 3일은 무증상, 마른기침 정도

 

2. 이후 가슴 압박 시작

 

3. 갑자기 자다가 호흡곤란과 가슴통증 시작

 

4. 1339는 통화 불가, 동네보건소 전화했지만

 

5. 역시나 코로나 바이러스 아닌 것 같다고 거부

 

6. 다시 호흡곤란과 가슴통증 극심

 

7. 보건소에 다시 전화해서 대동병원으로 이동

 

8. 대동병원 검사 대기중 호흡곤란이 오면서 정신 잃고 쓰러짐

 

9. 입원기간 중 상태는..

 

9-1 두꺼운 철판이 가슴을 누르는 듯한 통증

 

9-2 약을 복용하면 목구멍과 가슴, 위장에 불타는 듯한 증상

 

9-3 피부 건조중이 아주 심하게 나타남

 

9-4 고열

 

위의 증상들이 하루에도 몇 번씩 악화와 완화를 반복함

일단 안 걸리는 게 상책ㄷㄷ / 일베

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재채기를 하고 감기 기운이 있을 때, 어떤 약을 먹어야 할까?

1. 발열이 있고, 땀은 흐르지 않지만 기침을 한다. ---- 마황탕.

마황탕은 체격이 건장하고 얼굴이 거무스레하며 잘 병에 걸리지 않는 사람에게 적합하다.

2. 발열이 있고 땀이 흐르고 찬바람을 싫어한다. ---- 계지탕.

계지탕은 평소에 추위를 타고 위장의 기능도 별로이고 허약한 사람에게 적당하다. 술을 많이 먹는 사람은 이 약이 맞지 않는다. 이 약은 상한론에 제일 처음으로 나오는 방제이다. 그만큼 중요하고 많이 쓰이는 기초적인 방제라는 뜻이다. 이 약에서 작약을 두 배로 하고, 거기에 엿을 넣으면 소건중탕이라고 부르는데, 허약하고 밥을 잘 먹지 않는 어린이에게 적합한 영양제이다. 때로는 허약한 사람의 감기에 소건중탕을 쓰기도 한다. 나는 겨울에는 이 계지탕을 늘 끓여놓고, 집안에 재채기를 하는 사람에게 준다. 경우에 따라 옥병풍산(황기, 백출, 방풍)을 합방해서 쓰기도 한다.

3. 발열이 있고 두통이 있으며, 목이 뻣뻣하다. --- 갈근탕

여기에서 핵심은 목이 뻣뻣하다는 점이다. 잠을 자고 난 뒤에 목이 불편한 사람도 갈근탕이나, 계지탕 + 갈근을 복용하면 해소가 된다. 갈근탕의 체질은 건장한 마황탕과 허약한 계지탕의 중간 정도라고 보면 된다.

4. 발열과 두통이 있고, 목도 불편한데, 거기에 더해 입이 쓴 사람.--- 九味羌活湯구미강활탕. 

表濕을 제거한다.

5. 발열이 있고, 팔다리가 아프고 쑤시며, 입이 쓰고, 약간 토할 것 같은 느낌도 있을 경우 --- 시호계지탕.

시호탕과 계지탕을 섞은 방제인데, 약의 양이 조금 다르므로 주의해야 한다.

 6. 발열이 있지만 춥다고 하고, 자꾸 누워 자려고 한다. ---마황부자세신탕

양기가 쇠한 노인들이나 중년 이상의 사람들에게 자주 나타나는 증상이다. 찬바람을 맞은 뒤에 춥다고 하고, 자꾸 잠을 자려고 하는 사람에게 가장 적합한 약이다. 저 약은 이름 그대로 마황, 부자, 세신 3가지 약으로만 구성되는데, 증세만 맞으면 1첩만으로도 신효할 정도로 잘 듣는다. 하지만 마황과 부자는 개인적으로 살 수가 없어서, 한의원에 가서 처방을 받아야 한다.

7. 발열과 해수가 있고, 가래는 희고 포말泡沫이 있다. ---소청룡탕

가래가 희다는 것은 일반적으로 환자의 몸 내부가 염증이 없고 차갑다는 뜻이다. 상한론에서는 內飲이 있는 경우 쓴다고 했다. 감기가 어느 정도 진행되어 기침을 할 때 쓴다.

저 약은 약국에서 과립제를 살 수 있다. 또 체질만 맞으면 과민성 비염에도 잘 듣는다. 아침 찬바람에 재채기를 하는 사람은 저 약을 먹어 볼만 하다.

8. 추웠다 더웠다 반복하고, 밥을 잘 먹으려 하지 않고, 옆구리가 그득한 느낌이 들고, 토할 것 같은 느낌이 있다. 또는 가슴이 답답하고 구토는 없고, 혹은 목이 마르고, 혹은 가슴이 아프고, 또는 기침을 한다. --- 소시호탕

 추웠다 더웠다 반복하고, 옆구리가 불편하고, 구토의 기가 있고, 기침을 한다는 게 요점이다. 여성들의 경우 생리 중에 걸린 감기에 이 소시호탕이 적당하다고 한다.

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나심 탈레브/ 가디언지

 

엉터리 학술 모델을 바탕으로 전염병 대유행의 대응 전략을 짠다면, 사람들은 죽을 것이다.

불확실성에 대처할 때는, 통치와 경계 모두 최악의 상황에 대비해야 한다.

 

The UK's coronavirus policy may sound scientific. It isn't

 

Dominic Cummings loves to theorise about complexity, but he’s getting it all wrong

 

 

When, along with applied systems scientist Dr Joe Norman, we first reacted to coronavirus on 25 January with the publication of an academic note urging caution, the virus had infected fewer than 2,000 people worldwide and fewer than 60 people were dead. That number need not have been so high.

 

At the time of writing, the numbers are 351,000 and 15,000 respectively. Our research did not use any complicated model with a vast number of variables, no more than someone watching an avalanche heading in their direction calls for complicated statistical models to see if they need to get out of the way.

 

We called for a simple exercise of the precautionary principle in a domain where it mattered: interconnected complex systems have some attributes that allow some things to cascade out of control, delivering extreme outcomes.

 

 

Enact robust measures that would have been, at the time, of small cost: constrain mobility. Immediately. Later, we invoked a rapid investment in preparedness: tests, hospital capacity, means to treat patients. Just in case, you know. Things can happen.

 

The error in the UK is on two levels. Modelling and policymaking.

 

First, at the modelling level, the government relied at all stages on epidemiological models that were designed to show us roughly what happens when a preselected set of actions are made, and not what we should make happen, and how.

 

The modellers use hypotheses/assumptions, which they then feed into models, and use to draw conclusions and make policy recommendations. Critically, they do not produce an error rate. What if these assumptions are wrong? Have they been tested? The answer is often no. For academic papers, this is fine. Flawed theories can provoke discussion. Risk management – like wisdom – requires robustness in models.

 

 

But if we base our pandemic response plans on flawed academic models, people die. And they will.

 

This was the case with the disastrous “herd immunity” thesis. The idea behind herd immunity was that the outbreak would stop if enough people got sick and gained immunity. Once a critical mass of young people gained immunity, so the epidemiological modellers told us, vulnerable populations (old and sick people) would be protected. Of course, this idea was nothing more than a dressed-up version of the “just do nothing” approach.

 

Individuals and scientists around the world immediately pointed out the obvious flaws: there’s no way to ensure only young people get infected; you need 60-70% of the population to be infected and recover to have a shot at herd immunity, and there aren’t that many young and healthy people in the UK, or anywhere. Moreover, many young people have severe cases of the disease, overloading healthcare systems, and a not-so-small number of them die. It is not a free ride.

 

This doesn’t even include the possibility, already suspected in some cases, of reccurrence of the disease. Immunity may not even be reliable for this virus.

 

Worse, it did not take into account that the duration of hospitalisation can be lengthier than they think, or that one can incur a shortage of hospital beds.

 

 

Second, but more grave, is the policymaking. No 10 appears to be enamoured with “scientism” – things that have the cosmetic attributes of science but without its rigour. This manifests itself in the nudge group that engages in experimenting with UK citizens or applying methods from behavioural economics that fail to work outside the university – yet patronise citizens as an insult to their ancestral wisdom and risk-perception apparatus. Social science is in a “replication crisis”, where less than half the results replicate (under exact same conditions), less than a tenth can be taken seriously, and less than a hundredth translate into the real world.

 

 

So what is called “evidence-based” methods have a dire track record and are pretty much evidence-free. This scientism also manifests itself in Boris Johnson’s chief adviser Dominic Cummings’s love of complexity and complex systems (our speciality) which he appears to apply incorrectly. And letting a segment of the population die for the sake of the economy is a false dichotomy – aside from the moral repugnance of the idea.

 

As we said, when one deals with deep uncertainty, both governance and precaution require us to hedge for the worst. While risk-taking is a business that is left to individuals, collective safety and systemic risk are the business of the state. Failing that mandate of prudence by gambling with the lives of citizens is a professional wrongdoing that extends beyond academic mistake; it is a violation of the ethics of governing.

 

The obvious policy left now is a lockdown, with overactive testing and contact tracing: follow the evidence from China and South Korea rather than thousands of error-prone computer codes. So we have wasted weeks, and ones that matter with a multiplicative threat.

 

• Nassim Nicholas Taleb

 

---중국과 한국의 예를 따르라고 했는데 거기에서 한국은 그리 본보기가 될만한 일이 없다. 중국은 중의학이라는 비밀 병기가 있었다.

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소위 전문가들도 단지 추측을 하고 있을 뿐이다.

예측을 하는 대부분의 사회과학자들은 그들의 모델이 미래를 보여주는 유리구슬이라고 믿고 싶어한다. 그들은 ‘추측’이라는 단어 대신에 “알고리듬”, 또는 “모델”이라는 그럴듯한 말을 쓴다.

하지만 아무리 많은 모델링을 하더라도, 아무도 미래를 예측하는 구슬을 갖고 있지 못하다는 것이 진실이다. 단지 모든 사람은 추측을 하고 있을 뿐이다.

개인의 자유는 단지 도덕적인 것만이 아니다. 그것은 지금과 같은 위기의 시기에 유용성을 제공하고 위험을 경감시켜 준다.

 

The "Experts" Have No Crystal Ball

 

Allan Stevo

 

On the night of Tuesday, November 8, 2016, and in the wee hours of Wednesday, November 9, 2016, Donald Trump became US president-elect. I am not aware of a single media source that predicted that. Many predicted Hillary Clinton would win. Some stayed out of the fortune-telling game.

 

The most striking thing was the lack of admission running up to election night that the experts were all making guesses. “Of course they were making guesses, no one has a crystal ball,” the rational observer might say, but petabytes have been dedicated to making the social sciences and all of their guesswork appear like, well, “science.” That means that it’s all very measurable and clear, when it actually isn’t.

 

Science, on the other hand, is science. Observable details can be used to solidify hypotheses into theory. Sometimes facts are even arrived at, though theory is usually the best a scientist can hope for.

 

And then there is the murky area between the two, where scientists use models to predict the future.

 

This starts to look a lot like when psephologist (election scientist), sabermetrician, and prognosticator Nate Silver in the weeks and months before the 2016 election replaced the term “algorithm” with the suggestion of fact, or referenced a peer-reviewed, proprietary model that one is supposed to believe tells the future. There is no question that the vast portion of social scientists who make predictions want their model to be seen as a crystal ball. "Algorithm" is a fancy word for "guess." "Model" is a fancy word for "guess."

 

The work is built on presumptions that are not philosophically or logically solid, but then a lot of math is used to cover up the illogical and unsteady foundations.

 

Hundreds of academic authors that I encounter over the course of a year cannot have a logically sound conversation or write logically sound arguments. They fail at the foundation of their arguments, yet proceed to built atop that unsteady foundation, knowing that few will notice. This is intellectual dishonesty, also known as lying. So rooted they are in the social scientist’s belief that their field is a science and that modeling can plausibly predict the future that some may not even be aware of the fundamental shortcomings of such a professional outlook. Yes, perhaps they themselves do not even notice the lie that they profess.

 

There are economists that put lots of math on top of bad presumptions. Metrics can be incredibly valuable in understanding a discipline or theory. When the logical foundation is faulty, though, the metrics may simply be window-dressing that adds legitimacy where none belongs. Complicated terminology can have the same purpose. Laymen have long understood that con-artist politicians, con-artist salesmen, and con-artist academics alike have opaque jargon, the use of which seems to demonstrate an unwillingness to be understood.

 

Warwick McKibbin and Roshen Fernando of Australian National University, the authors of a paper about the tens of millions who will die from coronavirus that has been widely cited in the media ("The Global Macroeconomic Impacts of COVID-19: Seven Scenarios") show off their crystal ball.

 

To their credit, the corona prognosticators did a good job of stating pretty clearly: "We don’t really know what we are talking about; we are really just guessing" (actual quote: "These results are very sensitive to the assumptions in the model, to the shocks we feed in and to the assumed macroeconomic policy responses in each countries [sic]"), and "Our scary death counts are not reliable and are not even the focus of our work or this paper, we are doing this to provide some sort of economic estimate for people to start working with" (actual quote: "The goal is not to be definitive about the virus outbreak but to provide important information about a range of possible economic costs of the disease. At the time of writing this paper, the probability of any of these scenarios and the range of plausible alternatives are highly uncertain. In the case where COVID-19 develops into a global pandemic, our results suggest that the cost can escalate quickly”).

 

They also openly admit, “Our intent is to profess political support for global bureaucratic structures in health and medicine and to call for shifts toward better funded and socialized medical systems” (Actual quote: “Many governments have been reluctant to invest sufficiently in their health care systems, let alone public health systems in less developed countries….This study indicates the possible costs that can be avoided through global cooperative investment in public health in all countries”).

 

The most important takeaway from the paper is not a prognostication, but an observation from the past, the notion that social contagion is the great ill that results from an outbreak, not the contagion itself: “From studying many outbreaks, the real risk is not the disease but public and governmental reaction to the fear of the disease,” (Actual quote: “The fear of an unknown deadly virus is similar in its psychological effects to the reaction to biological and other terrorism threats and causes a high level of stress, often with longer-term consequences (Hyams et al., 2002). A large number of people would feel at risk at the onset of a pandemic, even if their actual risk of dying from the disease is low.”)

 

If you read the 44-page paper, uncertainty around the data and biases are disclosed by the authors.

 

But the media took it and ran with it for a salacious headline. The Chicken Littles of the world ran with it. The politicians, emergency services, military, and public health bureaucrats, seeking greater control for themselves, ran with it.

 

This is all predictable. What is far from predictable is whether you or I will believe any of this salacious nonsense. We don’t need to fall for it. We can look at the prognostication of 15 million deaths by corona, along with anyone who cites it as if it were fact, and challenge them quietly or openly as a heretofore discredited and unreliable person using an unreliable source in a moment where reliable journalism and sources are so badly needed.

 

Generations, centuries, millennia of humans around the globe have known what November 2016 reminded an entire country of: no one has a crystal ball.

 

Let us not forget this valuable lesson so quickly.

 

Will you be a willing megaphone to Chicken Little? Will you laugh at the nonsense, or will you be among those who chase Chicken Little into the henhouse where he belongs?

 

Whatever you choose, any person lying to you—economist, social scientist, prognosticator—deserves the same skeptical treatment across disciplines, because charlatans find their way into every discipline.

 

No matter how much modeling they do, the underlying truth is that no one has a crystal ball. Everyone is guessing. The experts didn’t know in 2016 how the election would shape up. The experts don’t know now how corona will shape up. Don’t sacrifice basic liberties to an expert claiming to have a crystal ball. Don’t sacrifice your basic sense of self to an expert claiming to have a crystal ball. Don’t offer the power of your fear, the power of your belief, and certainly not the power of your trust to an expert claiming to have a crystal ball.

 

Skepticism is the proper tool to use with any holder of any crystal ball. That is true, regardless of the complexity of the algorithm that they claim shows irrefutable proof of what the future will bring one day from now, one year from now, or one century from now.

 

The future is unknown to us and investing all of a society’s options in one path is detrimental to a successful outcome. One way that free societies have long prospered is by allowing individuals to produce many varying paths, some of which work, some of which fail. That is the risk mitigation method of freedom and individual choice. A single, unified path that no one may oppose removes a great deal of risk mitigation and forces many to make a single bet on a single path. That is a truly foolish idea given the fact that no one has a crystal ball.

 

To believe in the utility of central planning you must be able to fall for the idea that someone somewhere has a crystal ball. Individual freedom is not only moral, it provides utility and risk mitigation in moments of crisis, precisely like the one we now face.

 

American federalism provides for fifty experiments. Individual freedom in America provides for 330 million experiments. Some win, some lose. That’s life. Authoritarianism that drags everyone down a common path merely ensures that all will eventually lose.

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