Standardize Me
The confusion is compounding.
Going down a path that starts with an inadequate task is the beginning of an assembly line of industrial-sized, overlapping, systemic problems.
— Nora Bateson, “Hallway of Hallways”
About 200,000 students took the Scholastic Assessment Test (SAT) this past weekend. My daughter was among them. Despite me reminding her it’s a bad test that all of the schools she wants to apply to made optional pre-pandemic, she’s been determined to score highly. Rory Gilmore might be an influence here.
For many years, the SAT and its cousin, the ACT, were considered predictors of future success—reliable indicators of one’s ability to excel in higher education and beyond. Rules and guidelines could be established based on scores. By putting a number on each student, an admissions officer could weed through the piles of applications on their desk more efficiently.
The research on whether higher standardized test scores correlate to better performance in higher education is mixed and often reflects the interests of those funding the research. Considering that other factors that correlate to better performance on standardized test scores, such as parents’ education level, income, and access to housing in preferred school districts, would also correlate with better performance in higher education.
My daughter also once told me that she prefers essay tests to multiple choice because an essay test gives her a chance to explain herself. It was a proud mama moment. Having read through some of the multiple-choice questions she’s encountered in her AP courses this year, I can understand why. These questions—or rather, the answers on offer—are almost impenetrable. To answer correctly, one must not only understand the material but also have integrated a particular way of describing the material. Performing well on this kind of test requires a student to assimilate to a standardized language pattern.
Standardized tests—whether general or particular—allow for efficient grading. If grading looks like feeding a test into a scantron machine, then grading takes mere moments. And even that reference is really dating me, because my daughter took the SAT on an iPad—no one is needed to feed the paper into the machine. Her scores, hypothetically, could have been available instantly. And she was quite frustrated that she had to wait two weeks to receive them.
Standardization is a powerful process. It improves efficiency, lowers prices, and allows for more predictable outcomes. But it’s not a panacea. When we standardize a system, whether it’s the admissions system at a college or the sizing system for retail clothing, we risk changing the moral calculus associated with that system. Whether a score is good or bad or whether a body is right or wrong quickly hinge on choices we make in establishing the standards of evaluation.
Standardization is on my mind for more reasons than my daughter’s SAT goals. It’s one of those ideas that will follow you everywhere once you’re attuned to it. Elon Musk’s approach to the federal bureaucracy hinges on standardization. The AI-ification of everything is a process of standardization. The steady stream of media being produced based on existing characters, worlds, and storylines is a consequence of standardization.
So today, I’m updating a piece I wrote back in November 2023.
Industrialization and standardization are two sides of the same coin. And if a coin can have three sides, I’d also add digitization. But we’ll get to that later.
Standardization made mass production possible…
…and mass production made standardization desirable.
As we became more and more reliant on machines to aid in the production of infrastructure, consumer goods, and other industrial products, we became more and more reliant on standardization. Industrialization and standardization brought us mass production. Maybe when you think “mass production," you think about a factory churning out boxes of cereal or t-shirts emblazoned with a logo. And yes, those cereal boxes and t-shirts are mass-produced. But mass production itself is a technology, a process designed for a particular purpose.
Mass production is a technology that standardizes each aspect of how something is made.
Take clothing as an example.
Before the mass production of clothing, your particular body measurements were turned into a pattern that was then turned into the item of clothing you needed. You might be the one doing the making, or you might have hired someone to make your clothes for you.
It wasn’t necessarily fine tailoring, but the clothing production process was specific for each person who needed clothes. For most of human history, you couldn’t waltz into a store and buy a garment off the rack. That whole experience is a recent invention—dating from the mid-1800s.
To get ready-made clothes, designers and manufacturers needed to imagine clothing in standardized sizes. They needed to dream up a manufacturing process based on those sizes. They needed to invent the kind of store that would sell standardized clothing. They weren’t doing it alone, of course. Industrialists all over the world were using standardization and mass production to imagine new ways to sell goods and meet needs.
Mass production as a technology made this imagining possible.
Standardization became a mental model so ingrained in how we think and act that today, it’s difficult to even see it as anything other than a natural process. But someone (really, many someones) started to think about how to produce goods faster and less expensively. Eventually, they realized that they could achieve their goal if they significantly reduced the variety of goods produced.
Machines became standardized, which led to raw materials becoming standardized, which led to products becoming standardized. With each new level of standardization, manufacturing could get a little faster and a little cheaper. And someone could make a little (or a lot) more money.
Mass production then inspired the standardization of labor.
By the early 1900s, Frederick Taylor figured out that not only could physical objects be standardized, but the various components of the labor process could be standardized, too. From the way a worker swung a hammer to the time it took to change tasks, every detail could be optimized and perfected across the factory floor.
Taylor's technology changed the worker.
Instead of an artisan, craftsperson, or skilled machine operator, the worker became a set of hands and a span of time. Just about anyone could be trained to use their set of hands and their span of time to get the same result. One worker could be easily replaced by thousands of other workers.
Scientific management, as Taylor’s technology came to be known, standardized human behavior at work. Again, manufacturing could get a little faster and a little cheaper. And again, some people made a little (or a lot) more money.
The development of technology, along with a workforce that learned to accept and embrace standardized behavior (in no small part thanks to the First and Second World Wars), continued to make goods faster and cheaper. By the mid-20th century, the American economy was beginning to revolve around consuming those mass-produced goods rather than making them.
In the emerging consumer economy, a new group of engineers and technologists came to prominence.
Then, mass production required the standardization of desire.
Marketers and ad men were engineers of affect rather than objects. They discovered a new opportunity for standardization: desire. They realized that by crafting compelling advertising, they could mold consumer demand to fit the products they were tasked with selling. Consumer desire became the new nexus for standardization.
Once again, making stuff got faster and cheaper because the costs associated with moving products out the door went down. And once again, some people made a little (or a lot) more money.
The advance of technology, labor, and desire standardization continued through the rest of the 20th century. Then, about 20 years ago, a new nexus for standardization earned the attention of people with plenty of money to invest.
That nexus was attention. And where our attention goes, our identity tends to follow.
Advertising for mass media relied on fairly universal needs. Everyone needs to eat, dress, clean, get places... and we're all open to ways to improve on those needs. Traditional advertising standardized our desires but largely left our identities intact—if only because we hadn't yet learned about all the different things we could pay attention to and, therefore, all the different identities we could hold.
The internet changed that. By connecting us through bulletin boards, IRC, chat rooms, blogs, and social media platforms, we discovered each other. And in discovering each other, we discovered ourselves—or rather, we discovered that our selves contained a host of identities.
I contain multitudes, as Whitman put it.
Now, software engineers-turned-speculators realized there was an opportunity. Everything we did online to express our various identities—liking, commenting, clicking, sharing—could be transformed into quantitative data. Data could tell advertisers who we were and what we might buy.
Quantitative data, of course, is a necessary prerequisite for standardization. It’s zeros and ones. It’s on or off. It fits in this row or that row.
Identities, however, don't come in standard sizes and shapes. I'm a weird mix of things. You're a weird mix of things. And any attempt to smooth out the rough edges changes who we are.
You know what happened next. Attention engineers began to figure out how to standardize us. Naomi Klein likens this to the process of enclosure.2 Enclosure is a legal technology for turning what was commonly held, like land, into private property. Klein argues that by locating more and more of our activity within enclosed digital platforms that turn that activity into data, we change. The way "we relate to one another and the underlying purpose of those relations" changes.
Imagine that you and I meet up at a coffee shop.
We both have our phones because, of course. We both have TikTok installed. Thanks to geotracking, TikTok knows our two phones were near each other for 30 minutes. There's a good chance we were together. That triggers a little handshake of data—my TikTok knows some new things about me, and your TikTok knows some new things about you—simply because we hung out. Over the next 24 hours, my FYP starts to look a little more like your FYP page. The ads I receive start to look a little more like the ads you receive.
Now, imagine that we meet up for coffee a month later, and we're both wearing the same sneakers, or raving about the same moisturizer, or obsessed with the same TikTokker. Sure, it could be a coincidence. Or, maybe we could have found our way to those same interests on our own. But maybe our coffee date set off a chain reaction that made us just a little more similar, a little more likely to watch the same content or buy the same products.
I don’t want this to sound like some kooky conspiracy theory.
In many ways, this interpersonal standardization process mimics millennia of social development among humans. Socialization is a form of standardization. The process itself isn't new—it's as old as we are. But what's new is the speed and scale at which it happens and the way it's used for financial gain. It's mass production on the level of human attention, which means we form and express our identities a little faster and a little more cheaply than we used to. And a few people make a little (or a lot) more money in the process.
Standardization is inherently reductive.
Digitization is inherently reductive. We are reduced, writes Zadie Smith in a 2010 review of The Social Network for The New York Review:
When a human being becomes a set of data on a website like Facebook, he or she is reduced. Everything shrinks. Individual character. Friendships. Language. Sensibility. In a way it’s a transcendent experience: we lose our bodies, our messy feelings, our desires, our fears. It reminds me that those of us who turn in disgust from what we consider an overinflated liberal-bourgeois sense of self should be careful what we wish for: our denuded networked selves don’t look more free, they just look more owned.
Smith reminds us, with the help of software pioneer Jaron Lanier, something we should never forget: “software is not neutral." Standardization isn't neutral.
Software is not neutral. Standardization isn’t neutral.
When women's clothing was finally standardized and mass-produced, decades after men's clothing, the pattern makers chose the shape and gradations of size that they did based on who they measured. They chose the ratio of hip measurement to waist measurement based on who they measured. They decided on the shoulder width to bra size ratio based on who they measured. They picked the length that skirts, pants, and sleeves should be based on who they measured.
And guess whose measurements didn’t count? Any woman who wasn’t white. Plenty of other groups of women were left out of the standardization process, too.
Perhaps early in this process, women were frustrated that there was now something wrong with the clothes they were supposed to buy. But it didn't take long for the clothes to always be right and the bodies to always be wrong.
Imagine how clothing sizes might be different if the standardization process had occurred in Africa or Asia instead of Europe and North America.
Any time we choose to record and categorize data, we choose how that recording and categorization will be done.
Depending on who you are, what your motives are, and the culture you come from, you'll make different choices than someone else would. You'll set up your database, choose the criteria, and start analyzing your data set. Your database will direct your focus. What you see in front of you is dependent on the initial choices you've made.
Here’s an example. Let's say you and I both set out to create a database of the hiking trails in the Flathead Valley. We each select criteria that we believe are critical to evaluating a hiking trail. I choose the length of the trail, the elevation change, and whether or not I need to carry bear spray. You choose the average time it takes to hike, the driving distance to the trailhead, and a 5-star rating of the sights along the trail. Then, we take the same data set—all of the trails in the Flathead Valley—and we start to fill in our database.
Once we have it all filled out, we filter and sort, filter and sort. Even though we started with the same inputs, we won't ever get our databases to reveal the information in the same way. I create a top-ten list based on my criteria. And you create a completely different top-ten list based on your criteria.
The trails are the same. But the choices we make with regard to the data make us see them differently.
Right now, there are attention engineers all over the world making decisions about what data to collect and how to categorize it.
Those choices seem like objective descriptions of what already is. But just like a standard clothing pattern can make my body wrong or a database of hiking trails can change where I decide to go next, the choices those attention engineers make change how I see myself.
The attention engineers leverage the vocabulary we have for describing reality. Some of the words we use describe our consumer preferences (e.g., budget-conscious baby boomers or DIY homeowners). Others describe our class, level of education, or political preferences. Still other words describe what we're trying to do and who we're trying to become (e.g., entrepreneur, writer, runner, etc.).
“What there are no words for is often un-seeable.”
This vocabulary helps us articulate who we are and what we're about. But it also limits how we perceive ourselves. That's the nature of language. When attention engineers further limit or concretize our vocabulary so that they can standardize the ways we describe ourselves, we are further reduced.
The logisticians and data brokers make their choices about what filters and categories to use. And we've learned to default to those same filters and categories. But we don't have to.
We can make our own choices about the categories we use to define ourselves and relate to the world.
Different categories and filters show us a different version of what’s possible. They help us define problems based on our values rather than the whims of capital. The categories and filters we decide on open up new territories of imagination.
Through our relationships, we aim our attention at new experiences and pioneer new concepts of identity. We can use new words. The internet makes this so much easier than it once was. Even now, connecting with new people and communities online broadens our vocabulary in truly remarkable ways.
Standardization has its place.
Even this very essay is a standardization of sorts. I’ve taken a jumble of thoughts about identity, language, business, and profit and produced a more or, probably, less coherent version of them, given the vocabulary I have. I write to you as an individual—but also as an audience, a standardization of interests and attention.
Standardization does help us produce things much faster and more cheaply—whether those things are t-shirts, computer chips, or essays. However, standardization is limited. When we forget that, we limit ourselves. When we remember that, we can use standardization to our advantage. We can refigure questions and refactor equations. We can look for the categories and filters we assume apply, throw them out, and invent new ones.
Personally, I don't have to rely on the categories others choose to put me in. The market might know me as a runner with plantar fasciitis or an amateur designer who is quick to buy a new font. And honestly? The market is right! But I'm more than that, too—so I have to pay attention to how I diverge from my market-ready data set.
Politically, I don't have to see only the problems that others choose to focus on. The market might prefer I see climate change as a consumer issue. It's in the market's interest for me to buy clean makeup and eco-friendly cleaning products and glass containers rather than frame the problem as one created by the machinations of the market itself. Whenever a social problem is presented to me, I can take a closer look at the data set and question whether there's a deeper issue at play.
Business-wise, I don't have to take the challenges in front of me at face value. What appears to be an issue of marketing might be an issue of price or product. What seems like a growth opportunity might be a chance to slow down and rest. But if I only see my business or career given the filters that marketers use to convince me they know what’s best, I’ll be forever stuck chasing their solutions.
Standardized tools, messages, categories, and filters are useful—but they never tell the whole story. How many different ways can you tell the story? How many different questions can you ask? How many ways can you interpret the information in front of you?
How much more information is hidden?
There is always another way to see and sense the world.
