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Should Bernie Sanders drop socialism?
The term socialism makes political discourse difficult. Should we do away with it altogether?
- Politicians such as Bernie Sanders and Alexandria Ocasio-Cortez have self-styled themselves as socialists.
- Linguist John McWhorter argues the term is so polluted that even well-intented usage will make critical discourse impossible.
- He recommends leftists drop the liberal and socialist labels and transition fully to progressives.
Bernie Sanders is on a mission to save socialism. Not the concept, the term. He has described his politics as democratic socialist for decades. His popularity in the 2016 Democratic primaries saw a resurgence of the label among Americans, especially millennials. And last month he delivered a speech titled — with no sense of Calvin and Hobbes-style irony — "How Democratic Socialism Is the Only Way to Defeat Oligarchy and Authoritarianism."
But Sander's battle is being fought on two fronts. On the left front, socialists' organizations argue he has misappropriated their label, while the right front has surged with moral panic over socialism's burgeoning popularity.
As President Trump said at the 2019 Conservative Political Action Conference, "Democrat lawmakers are now embracing socialism. Socialism is not about the environment. It's not about justice. It's not about virtue. Socialism is about only one thing. It's called 'power for the ruling class.' That's what it is."
These competing ideas and definitions have left many confused, and since 2016, article after article, ad nauseam, has been penned to try to untangle socialism's lexical mess. At the 2019 Aspen Ideas Festival, linguist John McWhorter pitched a novel solution. Why don't we just toss the word out?
How do we define socialism? (Depends on who you ask.)
Many people still understand socialism in its Cold War context, but the term has evolved much since. (Photo: Wikimedia Commons)
Before we get into McWhorter's argument, it's worth exploring said lexical mess.
In fairness to Sanders, he has been clear in how he defines democratic socialism. As he explained at a Georgetown University speech in 2015:
Democratic socialism means that we must create an economy that works for all, not just the very wealthy. Democratic socialism means that we must reform a political system in America today which is not only grossly unfair but, in many respects, corrupt.
For Sanders, democratic socialism offers a mixed-economy system to level the social playing field for all. It's the system we have today, but with some quality-of-life upgrades such as universal health care, free college tuition, and a guaranteed living wage.
The problem is that Sander's definition incorporates a free market and private ownership of production, both principles that stand in direct opposition to traditional definitions of socialism and democratic socialism. "To me, socialism doesn't mean state ownership of everything, by any means. It means creating a nation, and a world, in which all human beings have a decent standard of living."
Confused? Big Think writer Scotty Hendricks wrote a handy guide to socialism and its variants as typically defined by political philosophers. Here's a quick rundown:
Socialism. An economic system where the means of production are socially owned, as opposed to capitalism. In some understandings, the state does the owning and takes care of people's needs; in others, worker cooperatives or communes perform that task.
Democratic Socialism. The means of production are socially owned by the state, cooperative, or commune, but such means are managed democratically.
Social democracy. A mixed economy with a free-market system. However, the government regulates the market and may enforce control over some portions of the economy. It is sometimes called the Nordic model.
Yes, what Sanders calls democratic socialism typically goes by social democracy, and it's worth noting that labels like social liberal and New Dealer could potentially apply, too.
National Socialism. Socialism in this context is vestigial. The term was added to the National Socialists German Worker's Party (i.e., the Nazis) name to draw in leftist support. But once in power, the Nazis pursued a virulently anti-socialist policy by dissolving trade unions, ending social welfare programs, etc. If someone tells you the Nazis were socialists, remember that national socialism is the adopted sibling of the socialism family. It's related by name, not by blood.
This doesn't even begin to consider socialism's relationship with communism, which is a whole other headache to consider.
The case against Bernie Sanders use of socialism
This brings us back to McWhorter. Surveying socialism's historical baggage and many usages, he concluded the label is too polluted to be anything but toxic in American politics. It reminds us of the Soviet Union, he notes, and is associated (if unfairly) with the Nazi menace. And it's just plain unpatriotic.
"Wherever there's socialism, it's always a cloudy day," McWhorter said. "We shouldn't think of it that way probably, but that doesn't mean that we don't, and it doesn't mean that we can necessarily change it."
In recent years, Gallup has tracked American's feelings toward socialism. Its 2018 poll showed that fewer than half of Americans, age 30 and older, viewed socialism positively. The older an American, the less likely they were to have a favorable view of the word.
Conversely, capitalism was seen positively among most Americans though, again, more favorably among older Americans.
It's as though Americans are still channeling William F. Buckley when he said: "The problem with socialism is socialism. Because there are no socialists. Socialism is a system based upon an assumption about human nature that simply isn't true."
But McWhorter has his banhammer aimed at more than just socialism. He also argued the word liberal has become equally elusive. Like socialism, liberalism has been renamed, redefined, and repackaged in innumerable ways – social liberalism, neoliberalism, classical liberalism, progressive liberalism, radical liberalism (need we go on?). With each qualifier or semantic change, the term becomes more difficult to parse and makes discourse fraught with confusion.
Enough is enough, McWhorter argues.
What McWhorter is and isn't saying
First, McWhorter is not arguing against any ideas espoused as liberal or socialist. He is merely arguing that the terms themselves obfuscate rather than elucidate.
Second, McWhorter admits that his is an odd stance for a linguist to take. Typically, linguists are content to sit and watch as words shift, drift, and evolve through languages. It is what words do, and like a nature photographer, linguists record the process. They don't make judgments or interfere.
But McWhorter believes these terms have a uniquely destructive effect on our political discourse:
I think we need to talk about things in a new way. I think that we could have more time to talk about real things if we didn't have these ancient, and frankly effed up, terms gumming up the works and allowing unscrupulous people to make accusations that don't actually make any sense, but which have to be discussed leaving less time to talk about the crisis that our nation is in.
If not socialism, what term should Bernie Sanders adopt?
The Chinese Progressive Association protesting at a Stop ICE Rally. (Photo: Pax Ahimsa Gethen/Wikimedia Commons)
According to McWhorter, we already have a perfectly good word waiting in the wings, progressive. Unlike socialism, progressive is not weighted down by overly complex semantic change (yet) nor does it require listeners to perform tricky "mental acrobatics." It would further alleviate confusion between liberal and libertarian — both of which stem from the Latin liber-, meaning "free," but represent contrasting ideals — as well as nonpolitical uses, as in "liberal arts education."
As a bonus, the word is "etymologically transparent." Progressive means someone who wants "to progress" the current status quo. It contrasts nicely with conservative, someone who wants "to conserve" the status quo.
Across the board adoption of progressive would simplify our political discourse, streamline conversation, and make it so that bad actors can't willfully play an ideological shell game (see President Trump's comment above).
Can socialism be saved?
Earlier this year, I wrote an article wondering whether Sanders and Alexandria Ocasio-Cortez could redefine socialism in the U.S. There is some evidence that they are managing it.
The previously mentioned Gallup poll showed that more young Americans (age 18-29) hold a positive view of socialism than capitalism. Another Gallup poll found that more Americans today define socialism as meaning "equality and equal rights" versus "government ownership of the means of production." For both the tipping point came in 2016, when Sanders started his presidential bid.
McWhorter acknowledges that word definitions can ameliorate over time, but typically they drift into pejorative territory. Nefarious used to simply mean "famous" before coming to mean "wicked." Writing for the Atlantic, he points out that neoliberal originally separated middle-of-the-road conservatives from laissez-faire capitalists. Today, the word is little more than a "knee-jerk slur" used by the left to describe far-right conservatives.
Can socialism be saved? Maybe, but the struggle is uphill and the outcome unlikely. McWhorter's solution is far simpler, far less messy, and offers a change our political discourse desperately needs. Maybe it's time Bernie Sanders drop the socialism label.
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Scientists discover what our human ancestors were making inside the Wonderwerk Cave in South Africa 1.8 million years ago.
- Researchers find evidence of early tool-making and fire use inside the Wonderwerk Cave in Africa.
- The scientists date the human activity in the cave to 1.8 million years ago.
- The evidence is the earliest found yet and advances our understanding of human evolution.
One of the oldest activities carried out by humans has been identified in a cave in South Africa. A team of geologists and archaeologists found evidence that our ancestors were making fire and tools in the Wonderwerk Cave in the country's Kalahari Desert some 1.8 million years ago.
A new study published in the journal Quaternary Science Reviews from researchers at the Hebrew University of Jerusalem and the University of Toronto proposes that Wonderwerk — which means "miracle" in Afrikaans — contains the oldest evidence of human activity discovered.
"We can now say with confidence that our human ancestors were making simple Oldowan stone tools inside the Wonderwerk Cave 1.8 million years ago," shared the study's lead author Professor Ron Shaar from Hebrew University.
Oldowan stone tools are the earliest type of tools that date as far back as 2.6 million years ago. An Oldowan tool, which was useful for chopping, was made by chipping flakes off of one stone by hitting it with another stone.
An Oldowan stone toolCredit: Wikimedia / Public domain
Professor Shaar explained that Wonderwerk is different from other ancient sites where tool shards have been found because it is a cave and not in the open air, where sample origins are harder to pinpoint and contamination is possible.
Studying the cave, the researchers were able to pinpoint the time over one million years ago when a shift from Oldowan tools to the earliest handaxes could be observed. Investigating deeper in the cave, the scientists also established that a purposeful use of fire could be dated to one million years back.
This is significant because examples of early fire use usually come from sites in the open air, where there is the possibility that they resulted from wildfires. The remnants of ancient fires in a cave — including burned bones, ash, and tools — contain clear clues as to their purpose.
To precisely date their discovery, the researchers relied on paleomagnetism and burial dating to measure magnetic signals from the remains hidden within a sedimentary rock layer that was 2.5 meters thick. Prehistoric clay particles that settled on the cave floor exhibit magnetization and can show the direction of the ancient earth's magnetic field. Knowing the dates of magnetic field reversals allowed the scientists to narrow down the date range of the cave layers.
The Kalahari desert Wonderwerk CaveCredit: Michael Chazan / Hebrew University of Jerusalem
Professor Ari Matmon of Hebrew University used another dating method to solidify their conclusions, focusing on isotopes within quartz particles in the sand that "have a built-in geological clock that starts ticking when they enter a cave." He elaborated that in their lab, the scientists were "able to measure the concentrations of specific isotopes in those particles and deduce how much time had passed since those grains of sand entered the cave."
Finding the exact dates of human activity in the Wonderwerk Cave could lead to a better understanding of human evolution in Africa as well as the way of life of our early ancestors.
If you ask your maps app to find "restaurants that aren't McDonald's," you won't like the result.
- The Chinese Room thought experiment is designed to show how understanding something cannot be reduced to an "input-process-output" model.
- Artificial intelligence today is becoming increasingly sophisticated thanks to learning algorithms but still fails to demonstrate true understanding.
- All humans demonstrate computational habits when we first learn a new skill, until this somehow becomes understanding.
It's your first day at work, and a new colleague, Kendall, catches you over coffee.
"You watch the game last night?" she says. You're desperate to make friends, but you hate football.
"Sure, I can't believe that result," you say, vaguely, and it works. She nods happily and talks at you for a while. Every day after that, you live a lie. You listen to a football podcast on the weekend and then regurgitate whatever it is you hear. You have no idea what you're saying, but it seems to impress Kendall. You somehow manage to come across as an expert, and soon she won't stop talking football with you.
The question is: do you actually know about football, or are you imitating knowledge? And what's the difference? Welcome to philosopher John Searle's "Chinese Room."
The Chinese Room
Searle's argument was designed as a critique of what's called a "functionalist" view of mind. This is the philosophy that argues that our mind can be explained fully by what role it plays, or in other words, what it does or what "function" it has.
One form of functionalism sees the human mind as following an "input-process-output" model. We have the input of our senses, the process of our brains, and a behavioral output. Searle thought this was at best an oversimplification, and his Chinese Room thought experiment goes to show how human minds are not simply biological computers. It goes like this:
Imagine a room, and inside is John, who can't speak a word of Chinese. Outside the room, a Chinese person sends a message into the room in Chinese. Luckily, John has an "if-then" book for Chinese characters. For instance, if he gets <你好吗>, the proper reply is <我还好>. All John has to do is follow his instruction book.
The Chinese speaker outside of the room thinks they're talking to someone inside who knows Chinese. But in reality, it's just John with his fancy book.
What is understanding?
Does John understand Chinese? The Chinese Room is, by all accounts, a computational view of the mind, yet it seems that something is missing. Truly understanding something is not an "if-then" automated response. John is missing that sinking in feeling, the absorption, the bit of understanding that's so hard to express. Understanding a language doesn't work like this. Humans are not Google Translate.
And yet, this is how AIs are programmed. A computer system is programmed to provide a certain output based on a finite list of certain inputs. If I double click the mouse, I open a file. If you type a letter, your monitor displays tiny black squiggles. If we press the right buttons in order, we win at Mario Kart. Input — Process — Output.
Can imitation become so fluid or competent that it is understanding.
But AIs don't know what they're doing, and Google Translate doesn't really understand what it's saying, does it? They're just following a programmer's orders. If I say, "Will it rain tomorrow?" Siri can look up the weather. But if I ask, "Will water fall from the clouds tomorrow?" it'll be stumped. A human would not (although they might look at you oddly).
A fun way to test just how little an AI understands us is to ask your maps app to find "restaurants that aren't McDonald's." Unsurprisingly, you won't get what you want.
The Future of AI
To be fair, the field of artificial intelligence is just getting started. Yes, it's easy right now to trick our voice assistant apps, and search engines can be frustratingly unhelpful at times. But that doesn't mean AI will always be like that. It might be that the problem is only one of complexity and sophistication, rather than anything else. It might be that the "if-then" rule book just needs work. Things like "the McDonald's test" or AI's inability to respond to original questions reveal only a limitation in programming. Given that language and the list of possible questions is finite, it's quite possible that AI will be able to (at the very least) perfectly mimic a human response in the not too distant future.
What's more, AIs today have increasingly advanced learning capabilities. Algorithms are no longer simply input-process-output but rather allow systems to search for information and adapt anew to what they receive.
A notorious example of this occurred when a Microsoft chat bot started spouting bigotry and racism after "learning" from what it read on Twitter. (Although, this might just say more about Twitter than AI.) Or, more sinister perhaps, two Facebook chat bots were shut down after it was discovered that they were not only talking to each other but were doing so in an invented language. Did they understand what they were doing? Who's to say that, with enough learning and enough practice, an AI "Chinese Room" might not reach understanding?
Can imitation become understanding?
We've all been a "Chinese Room" at times — be it talking about sports at work, cramming for an exam, using a word we didn't entirely know the meaning of, or calculating math problems. We can all mimic understanding, but it also begs the question: can imitation become so fluid or competent that it is understanding.
The old adage "fake it, 'till you make it" has been proven true over and over. If you repeat an action enough times, it becomes easy and habitual. For instance, when you practice a language, musical instrument, or a math calculation, then after a while, it becomes second nature. Our brain changes with repetition.
So, it might just be that we all start off as Chinese Rooms when we learn something new, but this still leaves us with a pertinent question: when, how, and at what point does John actually understand Chinese? More importantly, will Siri or Alexa ever understand you?
With the rise of Big Data, methods used to study the movement of stars or atoms can now reveal the movement of people. This could have important implications for cities.
- A treasure trove of mobility data from devices like smartphones has allowed the field of "city science" to blossom.
- I recently was part of team that compared mobility patterns in Brazilian and American cities.
- We found that, in many cities, low-income and high-income residents rarely travel to the same geographic locations. Such segregation has major implications for urban design.
Almost 55 percent of the world's seven billion people live in cities. And unless the COVID-19 pandemic puts a serious — and I do mean serious — dent in long-term trends, the urban fraction will climb almost to 70 percent by midcentury. Given that our project of civilization is staring down a climate crisis, the massive population shift to urban areas is something that could really use some "sciencing."
Is urbanization going to make things worse? Will it make things better? Will it lead to more human thriving or more grinding poverty and inequality? These questions need answers, and a science of cities, if there was such a thing, could provide answers.
Good news. There already is one!
The science of cities
With the rise of Big Data (for better or worse), scientists from a range of disciplines are getting an unprecedented view into the beating heart of cities and their dynamics. Of course, really smart people have been studying cities scientifically for a long time. But Big Data methods have accelerated what's possible to warp speed. As "exhibit A" for the rise of a new era of city science, let me introduce you to the field of "human mobility" and a new study just published by a team I was on.
Credit: nonnie192 / 405009778 via Adobe Stock
Human mobility is a field that's been amped up by all those location-enabled devices we carry around and the large-scale datasets of our activities, such as credit card purchases, taxi rides, and mobile phone usage. These days, all of us are leaving digital breadcrumbs of our everyday activities, particularly our movements around towns and cities. Using anonymized versions of these datasets (no names please), scientists can look for patterns in how large collections of people engage in daily travel and how these movements correlate with key social factors like income, health, and education.
There have been many studies like this in the recent past. For example, researchers looking at mobility patterns in Louisville, Kentucky found that low-income residents tended to travel further on average than affluent ones. Another study found that mobility patterns across different socioeconomic classes exhibit very similar characteristics in Boston and Singapore. And an analysis of mobility in Bogota, Colombia found that the most mobile population was neither the poorest nor the wealthiest citizens but the upper-middle class.
These were all excellent studies, but it was hard to make general conclusions from them. They seemed to point in different directions. The team I was part of wanted to get a broader, comparative view of human mobility and income. Through a partnership with Google, we were able to compare data from two countries — Brazil and the United States — of relatively equal populations but at different points on the "development spectrum." By comparing mobility patterns both within and between the two countries, we hoped to gain a better understanding of how people at different income levels moved around each day.
Mobility in Brazil vs. United States
Socioeconomic mobility "heatmaps" for selected cities in the U.S. and Brazil. The colors represent destination based on income level. Red depicts destinations traveled by low-income residents, while blue depicts destinations traveled by high-income residents. Overlapping areas are colored purple.Credit: Hugo Barbosa et al., Scientific Reports, 2021.
The results were remarkable. In a figure from our paper (shown above), it's clear that we found two distinct kinds of relationship between income and mobility in cities.
The first was a relatively sharp distinction between where people in lower and higher income brackets traveled each day. For example, in my hometown of Rochester, New York or Detroit, the places visited by the two income groups (e.g., job sites, shopping centers, doctors' offices) were relatively partitioned. In other words, people from low-income and high-income neighborhoods were not mixing very much, meaning they weren't spending time in the same geographical locations. In addition, lower income groups traveled to the city center more often, while upper income groups traveled around the outer suburbs.
The second kind of relationship was exemplified by cities like Boston and Atlanta, which didn't show this kind of partitioning. There was a much higher degree of mixing in terms of travel each day, indicating that income was less of a factor for determining where people lived or traveled.
In Brazil, however, all the cities showed the kind of income-based segregation seen in U.S. cities like Rochester and Detroit. There was a clear separation of regions visited with practically no overlap. And unlike the U.S., visits by the wealthy were strongly concentrated in the city centers, while the poor largely traversed the periphery.
Data-driven urban design
Our results have straightforward implications for city design. As we wrote in the paper, "To the extent that it is undesirable to have cities with residents whose ability to navigate and access resources is dependent on their socioeconomic status, public policy measures to mitigate this phenomenon are the need of the hour." That means we need better housing and public transportation policies.
But while our study shows there are clear links between income disparity and mobility patterns, it also shows something else important. As an astrophysicist who spent decades applying quantitative methods to stars and planets, I am amazed at how deep we can now dive into understanding cities using similar methods. We have truly entered a new era in the study of cities and all human systems. Hopefully, we'll use this new power for good.
A small percentage of people who consume psychedelics experience strange lingering effects, sometimes years after they took the drug.