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The last 10 years, explained

The (wild! scary! surprising!) moments that mattered.

06-BlurbsLede_d4d42b2015 to 2019. The surgery, where patients’ stomach fat is injected into their butts, has a sordid history embedded in Brazil’s eugenics movement and the hyper-sexualization of the mixed-race Black woman, known as the “mulata.” BBLs have also garnered headlines for their deadly health risks, mainly a result of fat embolisms. Nevertheless, it became hard not to notice the amount of Instagram influencers who had apparently gotten the surgery or, at least, were digitally enhancing their butts.

Then, just as quickly, it seemed like the tide had turned once again for the “ideal” female body. In 2022, a controversial New York Post article declared that “heroin chic” was back in. Social media observers also began noticing that Kardashian was suddenly a lot smaller. At the same, the diabetes drug Ozempic emerged as Hollywood’s latest weight-loss craze. Thus, the media eagerly questioned whether the BBL era was “over,” despite the surgery’s persisting popularity.

The question illuminated the ways Black people — their culture, their aesthetics, their literal bodies — are objectified and easily discarded under the white gaze. As Rachel Rabbit White wrote, “to celebrate the supposed ‘end of the BBL’ is synonymous with the desire to kill the ways in which Black women, especially Black trans women, and especially Black trans sex workers, have shaped the culture.” Writer Ata-Owaji Victor pondered where the rejection of this trend leaves “Black women and people who naturally have the ‘BBL’ body.” The answer is seemingly: in the same position Black women have always been put — useful until they’re not.

Kyndall Cunningham

The sweeping strike that put power back in teachers’ hands

In 2018, roughly 20,000 educators went on strike in West Virginia, protesting low pay and high health care costs. Their historic nine-day labor stoppage led to a 5 percent pay increase for teachers and school support staff.

With organizers galvanized by the victory in West Virginia, labor actions in states like Oklahoma, Kentucky, North Carolina, Colorado, and Arizona soon followed. According to federal statistics, more than 375,000 education workers engaged in work stoppages in 2018, bringing the total number of strikers that year to 485,000— the largest since 1986.

The uprising sparked national attention and enthusiasm both about the future of school politics and the possibility of resurging worker activism more broadly. It went by the shorthand “Red for Ed” — a reference to the red clothing educators and their allies wore every time they took to the streets.

The momentum continued the next year: In 2019, more than half of all workers in the US who went on strike came from the education sector, with new teacher actions spreading to states like Arkansas, Indiana, and Illinois.

Red for Ed changed the national political narrative

To be sure, the movement didn’t create lasting change in all aspects of education policy. Average teacher pay has stayed flat for decades, and fewer people are entering the teaching profession. Union membership writ large has continued to decline. And despite educators’ pushback against school privatization, conservatives managed to push through new expansions of public subsidies for private and religious schools following the pandemic.

But the teacher uprising earned the support of parents and the public, who reported in surveys strong backing for the educators’ organizing and for increased teacher pay. This strengthened support likely helped explain why parents largely stood by their kids’ teachers during the tough months of the pandemic, when educators again banded together for stronger mitigation standards to reduce the spread of Covid-19.

During the Obama era, a powerful bipartisan coalition for education reform spent much of their time attacking educators and their unions — a scapegoat for public education’s problems that most people ultimately did not buy. Red for Ed changed the national political narrative around teachers, and in many ways was a fatal nail in the coffin for that movement.

Rachel Cohen

Malaria in Maryland (and Florida, and Texas, and Arkansas) showed that the future of climate change is now

Last year, for the first time in two decades, mosquitoes transmitted malaria on American soil. The geographic range was unprecedented, with cases in Florida, Texas, Maryland, and Arkansas. 2023 was the hottest year on record since 1850, and for the mosquitoes that spread malaria, heat is habitat; the US cases occurred amid an uptick in malaria infections on a global scale.

Scientists have been warning us for years that without more public health resources, climate change was bound to push infectious threats into environments and populations unprepared for their consequences. Malaria’s reappearance in the US signaled to many that the future has arrived.

Wild weather turns previously inhospitable areas into ones newly suitable for lots of so-called vector insects to live. It’s not just different species of mosquitoes whose migration is changing disease trends. Ticks — different species of which spread diseases like Lyme, babesiosis, and ehrlichiosis — have progressively moved into new parts of the US in recent years as they’ve warmed. Changing weather patterns also cause many of these insects to reproduce in higher numbers in their usual habitats.

Insect habitats aren’t the only ones affected by climate change. Weather is pushing animals that serve as disease reservoirs into new environments, which can lead to more “spillover” events where germs get spread from one species to another. That’s thought to explain, at least in part, the fatal borealpox infection transmitted to an Alaska man by a vole bite last year; it’s also a concern when it comes to rabies transmission.

Extreme and unseasonable heat waves are also turning a progressively large part of the US into newly comfortable digs for fungi — including molds that cause severe lung and other infections in healthy people. Warming fresh and sea waters more frequently become home to noxious blooms of toxic algae and bacteria. What’s more, the heat is kicking pathogens’ evolution into overdrive: The microorganisms that can survive it are more likely than ever to also survive in our bodies, making them more likely to cause disease — and harder to fight.

As with many health risks, the consequences of climate-related infectious threats land hardest on the people with the fewest resources — and are almost incomparably worse in lower-resource countries than inside the US.

There’s a lot we still don’t understand about how climate change interacts with communicable diseases, including malaria. Some of the shifts caused by severe weather may reduce certain risks even as they amplify others. And disentangling the effects of severe weather from changes in policy, behavior, and human immunity, especially during and after a pandemic, is a formidable task.

Still, the comeback — or debut — of peculiar pathogens on American shores makes understanding these links viscerally urgent. Our warming planet isn’t going to wait until we’ve reformed and funded our public health system, seamlessly integrated disease surveillance into health care, renewed public trust in vaccines, and realigned incentives for novel antibiotic production before the fallout of climate change quite literally bites us in the ass.

Keren Landman

Letting language models learn like children tipped the AI revolution

Imagine you have a little kid. You want to teach them all about the world. So you decide to strap them to a chair all day, every day, and force them to stare at endless pictures of objects while you say, “That’s a banana, that’s a car, that’s a spaceship, that’s…”

That’s not (I hope!) how you would actually teach a kid, right? And yet it’s the equivalent of how researchers initially tried to teach AI to understand the world.

Until a few years ago, researchers were training AIs using a method called “supervised learning.” That’s where you feed the AI carefully labeled datasets. It actually yielded some decent results, like teaching AI models to tell apart a banana and a spaceship. But it’s very labor-intensive because humans have to label every bit of data.

Hopscotch with 0s and 1s in the squares.

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