Welcome back Mahathir Mohamad, Hero of Asia! (a)

This is a follow up to:

Welcome back Mahathir Mohamad, Hero of Asia!

Rishabh Gulat–who I respect greatly has a different take on Datuk Mahathir Mohamad, Hero of Asia, than I do. Some argue that Datuk Mahathir has recently shifted his policy and allied with conservative Wahhabi (subset of Salafi, subset of Sunni) muslims, MBS, Saudi Arabia and Pakistan against India. Mr. Gulat implies that Datuk Mahathir is backing Brown Pundit favorite Dr. Zakir Naik against India:

Please watch Mr. Gulat and come to your own conclusions.

The Indian Malays (7% of the population, 15% of the professional workforce, 40% of all Malaysian doctors, economic engine that moves Malaysia) are rallying the opposition to Datuk Mahatir. Mr. Gulat thinks the global Indian diaspora and global Eastern philosophy diaspora (presumably inclusive of Confucians, Toaists and Chinese) should back the Indian Malays in this.

I need to do a lot more research before proposing an alternative course of action. But here is a question. Can the Indian Malays, global Indian diaspora, global Eastern Philosophy, global Muraqabah tilted Sunnis and Shia and global liberal muslims unite and offer Datuk Mahathir Mohammed an offer he can’t refuse?:

There are many great and powerful Indian and Indonesian muslims–friends of PM Modi–who can make the offer.

As an aside, many Brown Pundits readers know Dr. Zakir Naik fanboy and heart throb Veedu Vidz. Please ask him to come on the Brown Pundits Podcast!

Mr. Rishabh Gulat is a great thought leader and expert on Indonesia, Malaysia and South East Asia more generally. He says that India and Indonesia should make a civilizational, cultural, economic and geopolitical alliance. Is there an interest in the Brown Pundits Podcast interviewing Mr. Rishabh Gulat?

Please let us know in the comments.

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American Caste (b)

America has a national crisis in math capacity, competence and merit. American students sharply underperform students in many countries all over the world. Including Vietnam, which is a poorer country than India per capita. We will heavily refer to the 2018 OECD PISA report in below paragraphs, but the below chart graphic is from the 2015 OECD PISA scores report because math scores are reported for more countries in the 2015 report. Perhaps the 2018 report will be revised to add more countries in the future:

In my view  a level 5 PISA score is the minimum requirement for a person to be considered a high school graduate who is literate in math, able to function in the modern global economy, or be qualified to attend college. The PISA report defines a level 5 PISA score or better as a fifteen year old that “can model complex situations mathematically, and can select, compare and evaluate appropriate problem-solving strategies for dealing with them.” How does America perform in the 2018 PISA report?:

  • United States: 8% of students scored at Level 5 or higher in mathematics
  • OECD average: 11%
  • Six Asian countries and economies had the largest shares of students who did so:
    • Beijing, Shanghai, Jiangsu and Zhejiang (China): 44%
    • Singapore: 37%
    • Hong Kong (China): 29%
    • Macao (China): 28%
    • Chinese Taipei: 23%
    • Korea: 21%

Note that these six countries were among the poorest countries in the world in the 1950s, far poorer than poor Americans or poor Europeans or poor Chileans can even imagine. In 1979 China was unbelievably poor. Much of the population of China–perhaps as many as 100 million–had starved to death because of extreme poverty in the 1970s. Poor children around the world are outperforming American children in mathematics despite extremely low education spending per student and very low socio-economic level of their legal guardians, where socio-economic level is defined as:

  • income
  • wealth
  • formal education of parents

Do any American high school student subgroups perform well in Mathematics? Yes, “people of color” or “minority” Americans perform well in Mathematics. America’s “people of color” or “minority” students are orders of magnitude more likely to get an 800 on the mathematics SAT than European Americans. If we assume this is an extreme tail end distribution issue related to European Americans having a lower standard deviation and non standard distribution in mathematics performance relative to “people of color” or “minority” Americans, we can explore the breakdown of Americans who score between 750 and 800 on the Mathematics SAT. Here European Americans perform far better relative to “people of color” or “minority” Americans.  In 2015 16,000 European Americans scored 750 or higher. 33,000 “people of color” and “minority” Americans scored 750 or higher. We further know that 51% of SAT test takers were European Americans and 49% were “people of color” or “minority” Americans.  “People of color” or “minority” Americans are [33,000/16,000]*[51%/49%] or 2.15 times as likely to score 750 or higher on the mathematics SAT compared to European Americans.  If we examine the 107,900 test takers who got SAT math scores of 700 or higher; 59,900 are “people of color” or “minority” Americans, versus 48,000 European Americans. “People of color” or “minority” Americans are [59,900/48,000]*[51%/49%] or 1.30 times as likely to score 700 or higher on the mathematics SAT compared to European Americans. For data junkie geeks like me there is a lot more data on SAT math score distributions here and here. The Greta Anderson article’s comment section in particular has some very intelligent commentators who have studied the American SAT score distribution. This is likely to be the subject of many future blog posts and Brown Pundits Podcasts.

What about this is worrying?:

  1. European Americans in particular are sharply under-performing both very poor children around the world and “people of color” and “minority” Americans in mathematics.
  2. American mathematics SAT scores have fallen between 1972 and 2016. 1972 is the earliest year for which I could find comparable SAT mathematics scores. In 2017, 2018 and 2019 the SAT mathematics exam was completely restructured to make scores no longer comparable to SAT mathematics scores between 1972 and 2016.
  3. 90% or more of current jobs and businesses are likely to be replaced by artificial intelligence (AI), brain electro-therapy (meditation . . . practiced by civilizations around the world for over 5,000 years), brain sound therapy (naad or mantra yoga and their equivalents in Native American, Egyptian, Sumerian, Taoist and other civilizations around the world for over 5,000 years), bio-engineering tissue, genetic editing, and fused AI-brain interface synthesis intelligence. Almost all of these future disciplines are complementary to mathematics.

Future articles and podcasts are planned all six of these future disciplines. If you are curious about fused AI-brain interface synthesis intelligence, please watch my main man Elon Musk:

Some say that the tension and relationship challenges between America’s four big castes–European Americans, European “Latino” Americans, Black Americans and Asian American–are driving low math scores for European Americans “AND” other Americans. One example is where thought leader Mark J Perry explores the possibility that tension between the European American caste and the Asian American caste are lowering American  mathematics performance. Excerpts of his article are reproduced below:

Continue reading “American Caste (b)”

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Post Modernism (d)

Five thousand years ago the greater Egyptian, Sumerian, Eastern (defined as pan Arya plus China) civilizations were very mathematically oriented.  Many caucasians appear to believe that these ancient civilizations were racist. Possibly because of this many caucasians believe that math is racist.

 

Another possible reason many caucasians appear to believe that math is racist is because they fear it might unfairly advantages “brown” people (Asians, Arabs, Latinos) and “brown” cultures (eastern philosophy including Toaism and Confucianism, native american religion) at the expense of caucasians in the new global artificial intelligence, neuroscience, genetics economy.

 

Could part of the anger against math come from fear that mathematics, science, technology, seeking the truth through thought, seeking the truth without thought might be haram or blasphemous? (Obviously most Abrahamics do not believe this and this is not a critique of Abrahamism.)

 

I believe that mathematics is part of art; and that it derives from beyond normal gross thought. From what in Sanskrit is called Buddhi, Vijnayamaya Kosha, Ananda Maya Kosha, Sukshma Sharira, Kaarana Sharira, the subtle heavens.

 

Perhaps the anger against mathematics is part of a deeper anger against the subtle heavens? If so, one possible way to look at this is that to transcend the subtle heavens (including mathematics) it might be helpful to love them and love our way through them. Or to love and respect the racist (subtle heavens–including mathematics) until we transcend the various subtleties of thought and feeling.

What are everyone’s thoughts?

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Post Modernism (c)

Post Modernism (b)

Post Modernism (a)

Intellectual Dark Web (a)

Intellectual Dark Web

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Is it time for Asian Americans and Latino Americans to ask to be considered “white”? (c)

This is the next article in the series “Is it time for Asian Americans and Latino Americans to ask to be considered “white”, “Is it time for Asian Americans and Latino Americans to ask to be considered “white” (a)”,  Razib’s  “Hasan Minhaj’s Patriot Act on Affirmative Action“, and “Is it time for Asian Americans and Latino Americans to ask to be considered “white” (b)”.

A growing part of the global caucasian intelligentsia are attacking Hong Kong protesters as far right fascists. This is part of a growing trend among xenophobic caucasians attacking Asians for “white supremacy”, “nazism”, “racism”, “oppression”, “patriarchy”, “imperialism”, “colonialism”, “hegemony”, “exploitation.”

Why is this happening? Is it just jealousy? Is it that many caucasians fear that “darkies” own a growing percentage of global wealth, earn a growing percentage of global income? Is it fear that “darkies” have growing competence, capacity, merit, mental health, intelligence? Is it fear about improving “darkie” academic outcomes?

I am not sure. Can everyone share their thoughts?

How should us “darkies” react?

I believe in loving and respecting our enemy with all our hearts, all our souls, all our minds and all our might. This includes everyone who is disrespectful, not loving, racist, bigoted, prejudiced, white supremacist, Nazi, facist, oppressive, hegemonic, exploitative, patriarchal towards us. And everyone who accuses us of being disrespectful, not loving, racist, bigoted, prejudiced, white supremicist, Nazi, facist, oppressive, hegemonic, exploitative, patriarchal. And everyone who labels and mislabels us. And everyone who falsely accuses us.

Everyone has the right to freedom of art and thought. If we truly love and respect others, then how can we not respect their right to disrespect and not love us?

The sweetness of love will gradually melt their hearts.

Some might say that this works for most people who are mean to others, but is insufficient for dangerous people. For particularly dangerous people, we can combine the deepest of love and respect with dialogue. And for the most dangerous people, we can combine love, respect, and dialogue with other things.

Can there be any other way?

This topic is one of the reasons The Brown Pundits Podcast would like to interview Irshad Manji:

Irshad Manji has touched the sweetness of the heart, the silence that is always with us. And while I agree with her that we should respect and love others, and not label others. I don’t think we have the right to limit the freedom of art and thought of others by asking them not to label and mislabel us.

One example that inspires me is how Krishna dealt with harsh bigotry, criticism, false allegations, others mislabeling him, disrespect, bigotry, prejudice, white supremacy, Nazism, fascism, oppression, hegemony, exploitation, patriarchy. Krishna insisted that others be allowed to criticize Krishna.

I would be curious to listen to Irshad Manji’s thoughts about this.

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India Still Rising (c)

One of the economists I follow is Rathin Roy [member of the Prime Minister’s Economic Advisory Council.] India has several major long term growth challenges. One is geographic inequality in growth. South and West India are growing much faster and have much lower population growth rates than the rest of India, causing them to pay far higher taxes than they recieve in government spending benefits. Some believe this could cause long term Indian instability. My view is that the poor parts of India are likely to grow rapidly in the future. When measured in terms of human population I think STs, SCs, OBCs and poor conservative Sunni (non Sufi) Indians are likely to experience rapid economic growth, causing this issue to take care of itself over time.

Rathin Roy is optimistic about short term Indian economic growth but worries about India’s long term economic growth.  He worries that India could enter the upper middle income country trap, similar to Brazil. Let us assume that income or Y depends on three inputs, K (Capital = tools or the sum total of all previous investments minus depreciation), L (Labor = total hours worked),  A (technology, product development and process innovation, total factor productivity):

Y = F(AL, K)

dY/dL = marginal product of Labor = long term real wages on average

dY/dK = marginal product of Capital = long term real rate of return on investment

India has a reasonable savings rate which finances investment.

India has a long term challenge with A or technology. What are these challenges?:

Continue reading “India Still Rising (c)”

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Intellectual Dark Web (b)

Eric Weinstein is close to the intersection of science, math and spirituality (or religion). He is skeptical that someone can do multi-dimensional math and science in deep meditation (satori, samadhi, mystical rapture). I think this is possible (not yet figured out how to do it).

Many ancients in narrative stories are described as combining science, math and spirituality. Including the great 7 sages (of the east, Sumeria and Egypt). Including Vishwamitra. Including Hinanyaksha and Hiranyakashipu.

I hope that our current crop of science and math thought leaders fully self actualize.

Eric describes the many theisms that different groups of people have, including in physics, math, AI, liberal arts, silicon valley, local governance, national governance, international institutions, globalization, politics.

One of the goals of religion is to transcend all theisms seeking the truth alone. The goal of religion is atheism. Theisms being:

  • irrationalities
  • unverified assumptions
  • patterns in the subconcious {Chitta}
  • habits
  • pre-religion
    • all methods and paths and preparation for religion included in religious literature, including all sounds, words, music and the various levels of meditation.

Eric is exceptionally good at breaking all theisms. Sadly those who break all icons and assumptions tend to get demonized. The Intellectual Dark Web–including Eric Weinstein–are being attacked as predicted by beautitudes Matthew 5:10-12:

  • Blessed are those who are persecuted because of righteousness, for theirs is the kingdom of heaven.
  • Blessed are you when people insult you, persecute you and falsely say all kinds of evil against you because of me.
  • Rejoice and be glad, because great is your reward in heaven, for in the same way they persecuted the prophets who were before you.

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Intellectual Dark Web (a)

Intellectual Dark Web

 

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Contemplating the weave of the world

    [ exploring various versions of how the world of concepts can itself be conceptualized ]

.

**

Have patience with me: Omar Ali has invited me to post here, an honor I greatly appreciate, and I am introducing myself.

I’m an outsider. I’m your guest, and I only just arrived.. To be precise, I’m a Brit, resident in the United States:

If I’m to write on BrownPundits, I need to you know how ignorant I am in many respects, before I shed some of what knowledge I do possess — and also to focus myself in the Brown direction, because this place is devoted to “a discussion of things brown” — and while I’ll no doubt wander far afield as I post, I want to acknowledge and honor the purpose of this blog as I introduce myself here.

**

My interest, my fascination, my obsession even, is with the weave of the world. And indeed, if my friends Omar Ali, Ali Minai, and Hasan Asif can be any indication, the Punditry of Brown extends intellectually across all of history, geography and genius, to encompass the world of ideas and the world world to which the ideas refer in their combined entirety..

And thus the weave of the thing. That’s how the Kathasaritsagara, or Ocean of the Streams of Story, comes in to my story. Somadeva Bhatta’s concept of the oceanic streams of story caught Salman Rushdie’s eye, and Rushdie reference to it —

He looked into the water and saw that it was made up of a thousand thousand thousand and one different currents, each one a different colour, weaving in and out of one another like a liquid tapestry of breathtaking complexity; and Iff explained that these were the Streams of Story, that each coloured strand represented and contained a single tale. Different parts of the Ocean contained different sorts of stories, and as all the stories that had ever been told and many that were still in the process of being invented could be found here, the Ocean of the Streams of Story was in fact the biggest library in the universe. And because the stories were held here in fluid form, they retained the ability to change, to become new versions of themselves, to join up with other stories and so become yet other stories; so that unlike a library of books, the Ocean of the Streams of Story was much more than a storeroom of yarns. It was not dead, but alive.

— it’s a universal mapping of the sort that enchants the likes of Jorge Luis Borges and Umberto Eco, librarians both, encompassing the realm of human thought in narrative terms. And it’s one subcontinewntal form of the universal map, or model, or metaphor — the Net of Indra in the Avataṃsaka Sutra would be another.

Outside the subcontinent — but well within the compass of Brown Punditry– there are other such metaphors for the whole of the whole. Teilhard de Chardin’s oosphere is another, as is Sir Tim Berners-Lee’s >World Wide Web, in which complex weave of thoughts we now find ourselves.

But for my own purposes, the most interesting figure of the whole, the universe as we are able to think and name it, conceptually speaking, is the Glass Bead Game as described by Hermann Hesse in his Nobel-winning novel of that name

**

My own personal predilections run from cultural anthropology through comparative religion to depth psychology, and from violence to peace-making. But that’s a huge sprawl at best, and to bring all that into some kind of focus, to learn how to map that immense territory, and the vaster universe beyond it, I turn not just to strong>Hesse’s novel, but particularly to the Game which he describes in that book:

The Glass Bead Game is thus a mode of playing with the total contents and values of our culture; it plays with them as, say, in the great age of the arts a painter might have played with the colors on his palette. All the insights, noble thoughts, and works of art that the human race has produced in its creative eras, all that subsequent periods of scholarly study have reduced to concepts and converted into intellectual values the Glass Bead Game player plays like the organist on an organ. And this organ has attained an almost unimaginable perfection; its manuals and pedals range over the entire intellectual cosmos; its stops are almost beyond number. Theoretically this instrument is capable of reproducing in the Game the entire intellectual content of the universe.

You’ll see how that description covers much the same ground as Rushdie’s description of the Kathasaritsagara, and Edward Tufte’s image of the Ocean of Story which I’ve placed at the top of this post could also be a depiction of Hesse’s great Game.

There are many voices in the Ocean, and many voices in the Game, and they are interwoven: they form which a musician would recognize as a polyphony — their concepts and narratives at times clashing as in musical counterpoint, at times resolving, at least temporarily, in a refreshing harmony.

And what better model of the world can we contemplate at this moment, that one in which a multitude of at times discordant voices wind their ways to concord?

**


[ above: conventional score, bar-graph score and keyboard recordings of JS Bach, contrapunctus ix

Johann Sebastian Bach is the master of contrapuntal music, and, be it noted, a great composer for and improviser on the organ. And it is Bach whose music I listen to as I approach the business of modeling the world of ideas.

My mantram ca 1999/2000 was:<To hold the mind of Bach..

Where Bach devises and holds in mind melodies that collide and cohere, I want us to hold thoughts in mind — at times clashing thoughts — and learn to weave them into a coherent whole..

That’s my approach to making the Glass Bead Game which Hesse conceptualized, playable. And my playable variants on Hesse’s Game, the HipBone family of games, will be the topic of my next few posts — thanks to the kind inquiries of my BrownPundit friends, and Omar’s generous invitation to me to post here.

And perhaps, if you’re interested, we’ll play a few rounds of my games, or explore across the world of ideas and your and my interests, what I’ve come to think of as the HipBone style of thinking..

___________________________________________________

Charles Cameron is a poet and game designer, managing editor of the Zenpundit blog, and now an invited guest at BrownPundits. You can hear a discussion of the overlap between the Glass Bead Game and Artificial Intelligence featuring Omar Ali, Ali Minai and myself on this BrownPundits podcast — with an appreciative bow to Razib Khan.

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Statistics on Asian American interracial marriage statistics

I really don’t know what to make of some of the contentions in Zach’s post below, What’s wrong with fetishizing white men? (also, posting videos which Hindi means I have no idea specifically what is going on in the video) Some of this is probably due to differences between the UK and the USA. But there are some statistics from the 2010 USA Census.

The website Asian Nation has tabulated the outmarriage rates by generation (foreign-born vs. US-raised) and various Asian American ethnicities. You can see the results below as I’ve repackaged them to focus on inmarriage of various subgroups, stratified by sex.

Some notes

1) “Asian Indian” only includes people who are Indian nationals or whose ancestors were from the Republic of India. It excludes other South Asian nationalities (I am not “Asian Indian” for example). But since other South Asian nationalities are a very small number in the USA I think that’s fine.

2) The statistics are generated from subsamples of the Census. I would be a bit cautious on outmarriage rates for groups like Asian Indians and Koreans where in 2010 the number of those born or raised in the USA was still rather small compared to the foreign-born/raised population. One reason Indian Americans showed extremely low outmarriage rates in the early 2000 Census results is that there was a massive swell of immigration in the Clinton era from Indians, so the foreign-born immigrants overwhelmed the signal.

3) Both the Japanese and Chinese have multi-generational communities in the United States. There are large numbers of highly assimilated Japanese and Chinese Americans whose roots in East Asia are as far back as their grandparents, or even earlier. I think it is noticeable that there is sex balance here.

4) I know a lot of you like bullshitting. I will be doing other things for a while so not monitoring comments much. But if I come back and have to see 1,0000-word personal thoughts which are factless and emotional I will just delete them, even if you are a long-time commenter.

Continue reading “Statistics on Asian American interracial marriage statistics”

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Indian Muslims are more latitudinarian than Pakistani Muslims

There is a lot of talk on this weblog. Probably because this is South Asian focus, and we tend to be a loquacious people on the whole (some more than others). But I decided to look in the World Values Survey in regards to the question of whether believers believed their religion was the only acceptable religion.

Before some of you ask about methods and cross-tabs, the website has a late 1990s interface. You too can use it and look up facts!

(also, Hindu intolerance surprised me a bit, though not too much)

Continue reading “Indian Muslims are more latitudinarian than Pakistani Muslims”

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(Machine) Learning Biases

Cross posting from Ali Minai’s excellent “Barbarikon” blog (this is, of course, Ali Minai’s writing, not mine)

 In a recent tweet, Congresswoman Alexandria Ocasio-Cortez – widely known as AOC – responded to a report from Amazon that facial recognition technology sometimes identified women as men when they have darker skin. She said:

When you don’t address human bias, that bias gets automated. Machines are reflections of their creators, which means they are flawed, & we should be mindful of that. It’s one good reason why diversity isn’t just “nice,” it’s a safeguard against trends like this

While I agree with the sentiment underlying her tweet, she is profoundly wrong about what is at play here, which can happen when you apply your worldview (i.e. biases) to things you’re not really familiar with. To be fair, we all do it, but here it is AOC, who is an opinion-maker and should be more careful. The error she makes here, though, is an interesting one, and get to some deep issues in AI.

The fact that machine learning algorithms misclassify people with respect to gender, or even confuse them with animals, is not because they are picking up human biases as AOC claims here. In fact, it because they are not picking up human biases – those pesky intuitions gained from instinct and experience that allow us to perceive subtle cues and make correct decisions. The machine, lacking both instinct and experience, focuses only on visual correlations in the data used to train it, making stupid errors such as relating darker skin with male gender. This is also why machine learning algorithms end up identifying humans as apes, dogs, or pigs – with all of whom humans do share many visual similarities. As humans, we have a bias to look past those superficial similarities in deciding whether someone is a human. Indeed, it is when we decide to override our natural biases and sink (deliberately) to the same superficial level as the machine that we start calling people apes and pigs. The errors being made by machines do not reflect human biases; they expose the superficial and flimsy nature of human bigotry.

There is also a deeper lesson in this for humans as well. Our “good” biases are not all just coded in our genes. They are mostly picked up through experience. When human experience becoming limited, we can end up having the same problem as the machine. If a human has never seen a person of a race other than their own, it is completely natural for them to initially identify such a person as radically different or even non-human. That is the result of a bias in the data (experience, in this case), not a fundamental bias in the mind. This is why travelers in ancient times brought back stories of alien beings in distant lands, which were then exaggerated into monstrous figures on maps etc. This situation no longer exists in the modern world, except when humans try to create it artificially through racist policies.

The machine too is at the mercy of data bias, but its situation is far worse than that of a human. Even if it is given an “unbiased” data set that includes faces of all races, genders, etc., fairly, it is being asked to learn to recognize gender (in this instance) purely from pictures. We recognize gender not only from a person’s looks, but also from how they sound, how they behave, what they say, their name, their expressions, and a thousand other things. We deprive the machine of all this information and then ask it to make the right choice. That is a huge data bias, comparable to learning about the humanity of people from distant lands through travelers’ tales. On top of that, the machine also has much simpler learning mechanisms. It is simply trying to minimize its error based on the data it was given. Human learning involves much more complicated things that we cannot even fully describe yet except in the most simplistic or metaphorical terms.

The immediate danger in handing over important decision-making to intelligent machines is not so much that they will replicate human bigotries, but that,within their limited capacities and limited data, they will fail to replicate the biases that make us fair, considerate, compassionate, and, well, human.

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