College students see inequalities while doing classes from home

Video conferencing software allows colleges classes to go on during COVID-19 but they can reveal differences between lives at home:

But as each logged in, not everyone’s new reality looked the same.

One student sat at a vacation home on the coast of Maine. Another struggled to keep her mother’s Puerto Rican food truck running while meat vanished from Florida grocery shelves. As one young woman’s father, a private equity executive, urged the family to decamp to a country where infections were falling, another student’s mother in Russia couldn’t afford the plane ticket to bring her daughter home…

She added: “It’s possible to believe that we can bridge inequalities by coming together on the Haverford campus, or that we can at least soften the edges — and then there is this incredible rupture. I’m very worried about what comes next for them.”

I suppose there is an optimistic and pessimistic way to look at this. For the first, perhaps college campuses truly do offer opportunities for students to have a somewhat level playing field. At the least, they have similar accommodations on campus and face similar day-to-day pressures regarding school. For the pessimistic side, on-campus college experiences may simply gloss over stark differences and access to resources while in school (as well as before and after). The campus experience might even make the problem worse by suggesting everyone has similar resources and opportunities.

Going further, there is a possible research study here looking at how students – and others using conferencing software for a variety of groups and organizations – display their surroundings. What are markers in a Zoom tableau or background that indicate relative advantage or disadvantage? How aware are users that they are doing this? Does it get discussed in the class/meeting/session or is it talked about later off-screen? What are the accepted norms in these areas?

From my own areas of research, I wonder what could be found regarding homes and interior spaces. Particularly for college students, where are the best or most common spaces for them to participate? American home activity can tend to center around the kitchen but I assume this is not the optimal space for video conferencing. This creates an interesting contrast: there are parts of homes that are meant to be showpieces for visitors – updated kitchens, big open concept spaces, entryways, the front exterior – but these would rarely show up on video conferences. If extended isolation becomes more common, would this change how people design homes and interior spaces?

5G over what percent of America? T-Mobile: covering over 5,000 cities and towns, 200 million Americans

T-Mobile is running a commercial touting their new 5G network. They claim it reaches 200 million Americans and over 5,000 cities and towns. What if we put those numbers in context?

On one hand, both figures sound impressive. Two hundred million people is a lot of people. This is a lot of text messages to send, TV shows and videos to stream, and social media and web pages to visit. This is a potential large market for T-Mobile. And 5,000 cities and towns sounds like a lot. I don’t know how many places Americans could name but many would probably struggle to name 5,000.

On the other hand, the figures suggest that the 5G coverage still does not reach a good portion of Americans or certain parts of the country. According to the Census Population Clock, the US population is over 329 million. So covering 200 million people comes to roughly 61% of Americans covered. This more than half, not quite two-thirds. Additionally, 5,000 cities and towns sounds like a lot. Some older data – 2007 – suggests the United States has over 19,000 municipal governments and the Census in 2012 also counted over 19,000. With these figures, 5G from T-Mobile covers a little more than one quarter of American communities.

Perhaps T-Mobile is doing the best the can with the coverage they have. The numbers are big ones and I would guess they could catch the attention of viewers. Maybe the numbers do not matter if they are trying to be first. However, just because the numbers are large does not necessarily mean the product is great. Significant segments of Americans will not have access, even with the big numbers. The numbers look good but they not be as good for some when they look into what they mean.

Mode, plurality, and “the most popular way”

I recently stumbled across this headline from Stanford News: “Meeting online has become the most popular way U.S. couples connect, Stanford sociologist finds.” Would the average reader assume this means that more than 50% of couples meet online?

This is not what the headline or the story says. More details from the story:

Rosenfeld, a lead author on the research and a professor of sociology in the School of Humanities and Sciences, drew on a nationally representative 2017 survey of American adults and found that about 39 percent of heterosexual couples reported meeting their partner online, compared to 22 percent in 2009.

It appears 39% of couples meet online. According to the summary of the paper, the others ways couples meet are:

Traditional ways of meeting partners (through family, in church, in the neighborhood) have all been declining since World War II.

The 39% figure meets the definition of both the mode and a plurality, respectively (both definitions from Google):

the value that occurs most frequently in a given set of data.

the number of votes cast for a candidate who receives more than any other but does not receive an absolute majority.

Still, I suspect there might be some confusion. Online dating brings more Americans together than any other method but it is only responsible for a little less than forty percent of couples.

Facebook releases big data to researchers outside the company

Researchers can now access a big dataset of Facebook sharing data:

Social Science One is an effort to get the Holy Grail of data sets into the hands of private researchers. That Holy Grail is Facebook data. Yep, that same unthinkably massive trove that brought us Cambridge Analytica.

In the Foo Camp session, Stanford Law School’s Nate Persily, cohead of Social Science One, said that after 20 months of negotiations, Facebook was finally releasing the data to researchers. (The researchers had thought all of that would be settled in two months.) A Facebook data scientist who worked on the team dedicated to this project beamed in confirmation. Indeed, the official announcement came a few days later…

This is a new chapter in the somewhat tortured history of Facebook data research. The company hires top data scientists, sociologists, and statisticians, but their primary job is not to conduct academic research, it’s to use research to improve Facebook’s products and promote growth. These internal researchers sometimes do publish their findings, but after a disastrous 2014 Facebook study that involved showing users negative posts to see if their mood was affected, the company became super cautious about what it shared publicly. So this week’s data drop really is a big step in transparency, especially since there’s some likelihood that the researchers may discover uncomfortable truths about the way Facebook spreads lies and misinformation.

See the codebook here and the request for proposals to use the data here. According to the RFP, the data involves shared URLs and who interacted with those links:

Through Social Science One, researchers can apply for access to a unique Facebook dataset to study questions related to the effect of social media on democracy. The dataset contains approximately an exabyte (a quintillion bytes, or a billion gigabytes) of raw data from the platform, a total of more than 10 trillion numbers that summarize information about 38 million URLs shared more than 100 times publicly on Facebook (between 1/1/2017 and 7/31/2019).  It also includes characteristics of the URLs (such as whether they were fact-checked or flagged by users as hate speech) and the aggregated data concerning the types of people who viewed, shared, liked, reacted to, shared without viewing, and otherwise interacted with these links. This dataset enables social scientists to study some of the most important questions of our time about the effects of social media on democracy and elections with information to which they have never before had access.

Now to see what social scientists can do with the data. The emphasis appears to be on democracy, political posts, and misinformation but given what is shared on Facebook, I imagine there are connections to numerous other topics.

More smartphones, more non-places

Place matters less when technology transports a user anywhere. Here is the argument from Ian Bogost:

This same pattern has been repeated for countless activities, in work as much as leisure. Anywhere has become as good as anywhere else. The office is a suitable place for tapping out emails, but so is the bed, or the toilet. You can watch television in the den—but also in the car, or at the coffee shop, turning those spaces into impromptu theaters. Grocery shopping can be done via an app while waiting for the kids’ recital to start. Habits like these compress time, but they also transform space. Nowhere feels especially remarkable, and every place adopts the pleasures and burdens of every other. It’s possible to do so much from home, so why leave at all?…

Architectural critics anticipated that modern life would change the sensation of space. Almost 30 years ago, the French anthropologist Marc Augé coined the word non-place to describe a family of transitional locations where people’s sense of self becomes suppressed or even vanishes. Non-places include airports, hotels, shopping malls, supermarkets, and highways. There’s a sorrow to these sites, because unlike legitimate ones, human beings never really occupy non-places; they simply move through them on their way to “anthropological places,” as Augé called them, such as schools, homes, and monuments.

Non-places have both proliferated and declined in the decades since. On the one hand, there are far more of them, and people encounter them more frequently. More airports and train stations in which more passengers transit more often. More hotel lobbies and conference centers, many boasting their own food courts and shopping plazas, non-places nested within non-places.

On the other hand, the anonymity and uselessness of non-places has been undermined by the smartphone. Every gate waiting area, every plush lobby couch cluster, every wood-veneered coffee shop lean-to has become capable of transforming itself into any space for any patron. The airport or café is also an office and a movie theater, a knitting club, and a classroom.

This same ability that can render places into a “non-place” could also be a feature of technology that users like the most: the ability to transcend time and place.

Based on this description of the term “non-place,” I wonder if modifying it might do better in regards to getting at the fluidity of so many spaces because of technology. Three options:

1. “Personalized non-place.” This would help capture the ability of an individual to make a place into whatever they want with a smartphone or another device. In a coffee shop, the person working on a laptop turns it into a personal office, another person talking with a friend turns it into a conversation space, and someone watching TV on their smartphone makes it a theater/viewing place.

2. “Ambiguous non-place.” This would get at the places that can be transformed by the people who come to them. Some places are more difficult than others to transform into whatever an individual or a group wants. Other places, those with places to walk, sit, eat, stay for a while, may be easier to transform by a variety of users.

3. “Fixed non-place.” This would get at places that are not transitional settings – hallways, highways, supermarkets – that are now non-places. Think the living room and family room, seating areas in more public settings, bedrooms. These are spaces we might assume people embody, develop attachments to, and nurture social relationship in but this does not happen in the same way now.

Eleven years in, self-driving cars are still a ways off

Transportation has changed in the last decade but self-driving cars will still take some more time:

The boldest bid to remake transportation with tech was also among the earliest, and so far, the most disappointing. In 2009, Google cofounder Larry Page tapped computer scientist Sebastian Thrun to build a self-driving car. Make a vehicle that moves people safely and efficiently, Page said (in Thrun’s telling), and you could have a business as big as Google itself. The resulting effort, now known as Waymo, helped trigger a global race for autonomy, one that many predicted would bear fruit by the decade’s end. Tesla CEO Elon Musk said a Tesla would drive itself across the country in 2017. General Motors promised to launch a robo-taxi service in 2019. Nissan targeted 2020 for the market debut of its self-driving car. Former Waymo lead Chris Urmson said he hoped his sons would never need to learn how to drive.

But billions of dollars and thousands of engineers haven’t produced a robot that can match, let alone eclipse, the ability of the human driver. AV developers have retreated to quiet suburbs and simple interstates, hoping they can master at least some corner of a profoundly complex world. GM pushed back its debut date indefinitely. Nissan has stopped talking about self-driving. Waymo is just starting to take the human backups out of its cars in the Phoenix suburbs. Musk never made his road trip.

Reading this brief overview, two things struck me:

  1. Having a computer do all that is needed to drive is a monumental task. There is a lot of information to take in from behind the wheel and the environment keeps changing. This makes human drivers look pretty good. Even with all the accidents and deaths that occur every year, that humans can handle all of this at 60 mph or higher is remarkable.
  2. All the money and effort that has gone into this simply reinforces the car as the primary agent of transportation in the United States. While having no human driver could be a game changer, all this effort does little to displace the car as center of social life, work, urban planning, and sprawl. Perhaps it would be too much to ask Americans to give up cars but this could be viewed by future Americans as a missed opportunity to reorganize society.

Even if the next decade features truly autonomous vehicles, it will take more time for these vehicles to work their way through the system. Since I have also seen lists of the new laws and regulations going into effect January 1, is it far-fetched to imagine a new rule starting in early 2025 that all new vehicles purchased must be fully autonomous?

Claim: “The physical environment feels depressingly finished”

As Derek Thompson of The Atlantic considers innovation and Silicon Valley, he includes this paragraph regarding innovation in the physical and urban realm:

And if you look up from your smartphone, progress becomes harder to see. The physical world of the city—the glow of electric-powered lights, the rumble of automobiles, the roar of airplanes overhead and subways below—is a product of late-19th-century and early-20th-century invention. The physical environment feels depressingly finished. The bulk of innovation has been shunted into the invisible realm of bytes and code.

There are several pieces that can be pulled out of this an examined:

1. Has innovation in cities and urban areas slowed? Many of the major changes may have already happened – think the modern skyscraper, the car and all the roads to go with them – but I’m guessing there are some lesser-known changes in the last few decades that have made a major difference. (For better or worst, one would be the global shift toward and innovations in capitalism, neoliberalism, and the finance industry that has had large effects on numerous cities and neighborhoods.)

2. If “the physical environment feels depressingly finished,” does this mean a change in aesthetics or style could alter this? Science-fiction films and shows tend to depict cities as white, gleaming, and move curved than they are today. Think Her which merges city life and technological change. Or, find images of cities from researchers, activists, and architects who imagine much greener cities full of plants and life rather than hard surfaces and cars. Perhaps the problem is not innovation as it is described in this article; one issue is that the look of big cities has not changed much in the fifty years or so (even as some individual buildings or projects might stand out).

3. If the look and feel of cities has not changed as much recently, could “the invisible realm of bytes and code” bring significant changes to the physical environment in the next few decades? In contrast to #2, perhaps future innovation in spaces will be less about collective experiences and aesthetics and more about changed private experiences. Imagine Virtual Reality in cities that allows pedestrians to see or overlay different information over their immediate surroundings. Or, easier access to Big Data in urban settings that will help individuals/consumers make choices.

Tech jobs continue to congregate in particular metropolitan regions

A new analysis looks at where tech jobs located between 2005 and 2017:

Researchers from the Brookings Institution and the Information Technology and Innovation Fund, a tech-industry-backed think tank, arrived at their conclusion by looking at a fairly narrow slice of jobs—13 industries that involve the highest rate of research and development spending and STEM degrees per worker. That includes much of the software industry, as well as jobs in areas like pharmaceuticals and aerospace. The researchers found that, between 2005 and 2017, five metro areas—San Jose, San Francisco, Seattle, San Diego, and Boston— not only added lots of jobs, they were also becoming more dominant in those industries overall.

TechJobsWired2005to2017

In part, that’s due to changes in what businesses need, says Enrico Moretti, an economist at UC Berkeley who wasn’t involved in the study. The enduring dominance of some tech hubs is somewhat counterintuitive. Technology was supposed to be a democratizing force—the internet and iPhone would make it possible to do innovative work from just about anywhere. But instead, high-tech industries became about proximity to your fellow high-tech workers. Businesses clustered around hubs of investment, in places where skilled workers could stick around after school, hop between jobs, and stay in touch with contacts. That plays out on an individual level too, Moretti says. In recent research tracking the patent activity of scientists as they moved in and out of places like the Bay Area, Moretti found that they were far more productive in those innovative hubs…

The researchers’ point is that it’s hard to build hubs of innovation from scratch—in places where the economy is really struggling, and where there’s little existing tech talent. Instead, you want to start with places that are already buzzing, and through a mix of investment—in things like R&D, education fellowships, and financing for small businesses—and tax incentives to encourage new business, nudge them to become innovation hubs. In other words, those places are already fertile ground for high-tech companies, but they need a little more fertilizer to get there. The researchers prefer federal investment to local subsidies that try to attract individual businesses—an often fruitless effort for smaller communities, as incidents like the downsized Foxconn factory in Wisconsin and Amazon’s HQ2 search demonstrate.

How exactly these centers of industry arise, thrive, and consolidate (and then maybe fade away or die?) is a good subject of academic study. Through a series of decisions, conditions, and good circumstances, agglomerations start. Inertia can carry them for a long time. As noted in the last paragraph, it can be difficult to introduce competition from other centers or create new centers once the main locations are well-established. Tech center do not just happen; they are the result of multiple social processes, interactions, and decisions.

Additionally, it is interesting to see that there is still a lot of value of actual physical locations near other businesses or organizations – even in a field that can render spatial and time distances less relevant. Being close to other people, being able to actually stop by or talk to them, still matters. All of this can add up to a location with a collection of similar organizations being more than the sum of its parts.

Yea! The Internet enables American workers to work more

A working paper links the number of hours American white-collar employees put in and the Internet:

In a new working paper, the economists Edward E. Leamer, of UCLA, and J. Rodrigo Fuentes, of Pontificia Universidad Católica de Chile, studied data about working hours from the American Community Survey. They found that hours worked since 1980 increased nearly 10 percent for Americans with bachelor’s and advanced degrees. Leamer told me that he believes this is because computing has shifted much of the economy from manufacturing to neurofacturing, Leamer’s term for intellectually intensive white-collar labor that is often connected to the internet, such as software programming, marketing, advertising, consulting, and publishing.

Neurofacturing jobs lend themselves to long hours for several reasons, Leamer said. They’re less physically arduous, as it’s easier to sit and type than to assemble engine parts. What’s more, the internet makes every hour of the day a potential working hour…

As Leamer and Fuentes write in the paper, “The innovations in personal computing and internet-based communications have allowed individual workers the freedom to choose weekly work hours well in excess of the usual 40.”

The internet has also supercharged global competition and forced international firms to outwork rivals many thousands of miles away. This has created a winner-take-all dynamic that’s trickled down to the workforce. In their 2006 study, “Why High Earners Work Longer Hours,” the economists Peter Kuhn and Fernando Lozano found that the premium paid for longer workweeks has increased since 1980 for educated workers, but not for less educated workers. Their theory is that at the most competitive firms, ambitious workers putting in super-long hours are sending a clear message to the boss: Promote me! And the boss isn’t just getting the message; he’s actively soliciting it. At many firms, insanely long hours are the skeleton key to the C-suite and the partner track. Thus, overwork becomes a kind of arms race among similarly talented workers, exacerbated by the ability to never stop working, even at home. It’s mutually assured exhaustion.

“Mutually assured exhaustion” is the result of zealous workers, managers asking more of employees, or the product of a unique work ethic in the United States?

This could lead to a basic question that I ask myself from time to time: has the Internet made life better? Is humanity thriving more, feeling better, doing more good, and experiencing a better life because of the Internet? The personal comparison is harder in that I was much younger when the Internet was not available but I can still imagine the comparisons. How might my academic work be different? My family life? My leisure time? And so on.

Additionally, the study also seems ripe for a comparison to other countries around the world that also have the Internet. Is the Internet the driver here or a tool that the American economic and social system utilizes to push a particular kind of work and approach to life? The Internet is not all powerful and cultural and social decisions in other societies seem to provide room for pushing against the possibility of working all day that the Internet allows.

Palaces for the People, Part 4: Facebook community versus physical community

I recently read Eric Klinenberg’s 2018 book Palaces for the People. Today, I highlight the last of four passages from the book that make some interesting connections regarding physical places.

Returning to social media toward the end of the book and specifically discussing Facebook, Klinenberg suggests online interaction is not a good substitute for interaction in real space in real time:

But no matter how the site’s designers tweak Facebook content, the human connections we need to escape danger, establish trust, and rebuild society require recurrent social interaction in physical places, not pokes and likes with “friends” online. (212)

This is a regular theme in the book: social media interaction cannot match social interaction that takes place face-to-face in real places.

I would guess social media platforms will try really hard in the next few years to change their platforms to encourage more positive social interactions. Some users already work hard to avoid negative interactions. Facebook, for example, is pushing community groups more. Instagram is hiding likes. Twitter is allowing people to hide responses to their posts. Will this all work? Possibly. But, Klinenberg argues that all of these efforts can only go so far. Humans will still need physical places that encourage interaction, trust, and new ideas.

Imagine social media in ten years that is primarily made up of positive interactions. Perhaps then it will be criticized for largely hiding negative emotions or conflict. Perhaps it be dull in the way that endlessly cheery stories might be. Or perhaps it will be seen as a supplement to offline relationships rather than competition for them.

Another way to think about Klinenberg’s ideas: what do public spaces need to be in order to entice people away from social media? There are ingredients that make public spaces more interesting such as a regular flow of people, a variety of activity, a human scale, and perceived safety. Do we have enough of these to truly people people way from their smartphones and if not, how much work would it take to develop spaces like this all over the country?