The most successful strategy for ending homelessness is under attack

“The housing-first model calls for providing individuals with permanent housing, but it doesn’t claim that housing alone is enough. Regular check-ins by trained case managers are required, as are making social and medical supports readily available.”

Did Redistricting Cost Democrats The House?

“The 2022 election for the House of Representatives was so close1 that if any number of things had gone differently, Democrats might have kept their majority. And one of the biggest things that affected the battle for the House was redistricting — the decennial redrawing of congressional districts’ lines to account for the results of the 2020 census.
But was the impact of redistricting significant enough to swing the House to the GOP? As I wrote in June, the 2021-22 redistricting cycle didn’t radically change the partisanship of the national House map, so I mostly agree with those who say redistricting didn’t cost Democrats the House. But at the same time, those who say Republicans won only because they gerrymandered are also technically correct. How can both things be true? Allow me to explain.

One way to test the claim that “redistricting cost Democrats the House” is to assess whether Democrats would have held onto the chamber if redistricting had never happened. We at FiveThirtyEight have already calculated how many percentage points each district swung left or right thanks to redistricting. For example, a district that went from a partisan lean2 of R+2 to D+3 got 5 points bluer. Then I compared this swing to the current 2022 House margin in that district.3 Suppose a party lost by less than the district swung away from that party in redistricting. In that case, it’s likely that redistricting cost that party the seat.

Of course, this is a hypothetical — and imperfect — exercise. Some districts changed substantially and wouldn’t have swung uniformly like that had they not been redrawn.4 In addition, if they had not changed, different districts might have attracted different candidates and different levels of spending from national groups, each of which could have affected the result. But this method can still give us a rough idea of what might have happened in a redistricting-less world.

Using this method, we can see that Republicans flipped a net six seats because of redistricting.”

“But Democrats also caught a few bad breaks in states with ostensibly nonpartisan redistricting processes. For example, the Arizona Independent Redistricting Commission made the 2nd and 6th districts5 about 10 points more Republican-leaning. In Michigan, the state’s Independent Citizens Redistricting Commission redrew the 10th District6 to be light red. And court-appointed experts nudged the New York 17th and Virginia 2nd rightward enough that they flipped too. Meanwhile, Democrats on the New Jersey Congressional Redistricting Commission voluntarily sacrificed the 7th District to protect vulnerable Democrats in other districts.

On the other hand, Democrats flipped a few seats thanks to redistricting. They drew some very Democrat-friendly maps in Illinois and New Mexico, enabling them to pick up the Illinois 13th and New Mexico 2nd. A court reconfigured North Carolina’s 13th District from a solidly red seat into a swing district that Democrats narrowly carried. And Republicans made the Ohio 1st District and Texas 34th District bluer, with the unfortunate (for them) side effect of handing those seats to Democrats.

But we also need to consider seats that didn’t flip but would have if redistricting had not occurred. And this is where Democrats benefited the most, gaining six seats on net — and canceling out Republicans’ gains from the flips that did occur.”

“Democrats also gained a net three seats from reapportionment, the process of subtracting congressional districts from states with sluggish population growth and giving them to states whose populations have exploded. Six of the seven districts that were eliminated by reapportionment were held by Republicans — slow-growth areas tended to be in rural and/or postindustrial areas, where Republicans usually dominate. But Republicans won only three of the seven districts that were created in reapportionment, for a net Democratic gain of three seats.”

“By my reckoning, Democrats actually gained three seats from redistricting overall. In other words, without redistricting, Republicans’ majority would be closer to 225-210.

“But wait,” I hear you saying. “There was no world in which redistricting wouldn’t have occurred in 2021-22. So isn’t it better to calculate how the 2022 election would have gone down if redistricting had gone differently, not if it hadn’t happened at all?” You have a point — but the problem is, there is no objective alternative map. The congressional map could have changed in a thousand ways depending on individual, state-level decisions.”

“[If redistricting went differently in a number of ways in favor of the Democrats,] Democrats probably would have won five more seats than they actually did.”

“five additional seats for Democrats would have been enough for them to hold onto a slim 218-217 majority. So yes, if every Republican gerrymander had been undone in court before the 2022 election, Democrats may have kept control of the House.

But that’s assuming no additional Democratic gerrymanders were thrown out in court.”

Americans Generally Support Unions — And Averting A Rail Strike

“For most of the time since the 1930s, a majority of Americans have favored labor unions, but support began to decline in the 1960s, dropping from 71 percent in 1965 to 55 percent by 1979. After a slight increase, Americans’ support of unions hit a low of 48 percent in 2009. The share of private-sector workers in unions also declined steadily since the 1980s. This was caused by a multitude of political and economic factors — industrial deregulation, the rise of anti-union politicians, increasing globalization — but American workplaces also fundamentally changed. Employment opportunities moved from traditionally organized workplaces, like factories, into a service industry where union density was already lower. Many workers unionizing today are making coffee instead of cars, and issues like high turnover and irregular worker schedules in those industries led to job instability.”

“Americans largely favor the kinds of worker protections and benefits unions fight for. In general, Americans think businesses should treat workers with respect, pay fair wages and provide health care benefits. Sixty-two percent of Americans support a $15 federal minimum wage, and three-quarters of Americans think the current federal minimum wage, $7.25 an hour, is too low. Americans strongly support paid family and medical leave, a sticking point in the rail-worker negotiations. While the pandemic led to more states and cities mandating paid sick leave and 79 percent of civilian workers had paid leave available to them as of March 2021, the workers least likely to have it are the lowest paid.”

Why Kyrsten Sinema Left The Democratic Party

“Facing a potential primary challenge on her left from Democratic Rep. Ruben Gallego, Sinema stood a real chance of losing renomination if she sought reelection as a Democrat (she might’ve been in trouble against a more center-left Democrat, too, like Rep. Greg Stanton). Tellingly, Yoshinaka’s study found the prospect of facing a highly competitive primary in one’s own party can play into leaving that party.”

“if Sinema’s chances of winning a Democratic primary were mediocre at best, it’s unclear how much stronger her path would be as an independent. It’s hard to imagine Republicans deciding not to field a major candidate against Sinema even if she’s an independent, but it’s possible she is hoping that the potential complications of a three-way race discourage a high-profile Democrat like Gallego from running. In that scenario, perhaps Democrats line up behind her in a head-to-head race against a Republican.
However, Gallego has already responded to Sinema’s switch by sending out fundraising texts that say he’s considering a Senate run. Now, Sinema might be able to put together a mishmash coalition of Democrats, Republicans and independents to win a three-way contest. After all, that Suffolk poll found that Republican likely voters also had a slightly more positive view of her than Democrats (35 percent favorable, 40 percent unfavorable), while independent likely voters had net-positive attitudes (42 percent favorable, 27 percent unfavorable). And she could attract plurality support if Democrats and Republicans nominate candidates who are viewed as too far left or right. That’s a possibility, too, as Gallego is a member of the Congressional Progressive Caucus, and Arizona Republicans just nominated far-right contenders Blake Masters and Kari Lake in the 2022 Senate and gubernatorial races, respectively.

But Sinema could certainly also find herself running in last place. Yoshinaka’s study found party switchers suffer an electoral penalty in their first general election after switching, with an average decline of 4 to 9 percentage points in vote share. Having upset Democrats, Sinema might lose most of their support to the Democratic pick, and there’s no guarantee that many Republicans back her over their party’s nominee, even if that candidate is highly problematic.”

“The difficulties Sinema is likely to encounter speak to why senators rarely switch parties, and why it’s even more unusual for them to become — and stay — independent. Sinema is just the 10th senator since 1951 to formally switch parties while in office”

In Defense of Algorithms

“When Facebook launched in 2004, it was a fairly static collection of profile pages. Facebook users could put lists of favorite media on their “walls” and use the “poke” button to give each other social-media nudges. To see what other people were posting, you had to intentionally visit their pages. There were no automatic notifications, no feeds to alert you to new information.
In 2006, Facebook introduced the News Feed, an individualized homepage for each user that showed friends’ posts in chronological order. The change seemed small at the time, but it turned out to be the start of a revolution. Instead of making an active choice to check in on other people’s pages, users got a running list of updates.

Users still controlled what information they saw by selecting which people and groups to follow. But now user updates, from new photos to shower thoughts, were delivered automatically, as a chronologically ordered stream of real-time information.

This created a problem. Facebook was growing fast, and users were spending more and more time on it, especially once Apple’s iPhone app store brought social media to smartphones. It wasn’t long before there were simply too many updates for many people to reasonably follow. Sorting the interesting from the irrelevant became a big task.

But what if there were a way for the system to sort through those updates for users, determining which posts might be most interesting, most relevant, most likely to generate a response?

In 2013, Facebook largely ditched the chronological feed. In its place, the social media company installed an algorithm.

Instead of a simple time-ordered log of posts from friends and pages you followed, you saw whichever of these posts Facebook’s algorithms “decided” you should see, filtering content based on an array of factors designed to suss out which content users found more interesting. That algorithm not only changed Facebook; it changed the world, making Facebook specifically—and social media algorithms generally—the subject of intense cultural and political debate.”

“Algorithms..help solve problems of information abundance. They cut through the noise, making recommendations more relevant, helping people see what they’re most likely to want to see, and helping them avoid content they might find undesirable. They make our internet experience less chaotic, less random, less offensive, and more efficient.”

“As Facebook and other social media companies started using them to sort and prioritize vast troves of user-generated content, algorithms started determining what material people were most likely to see online. Mathematical assessment replaced bespoke human judgment, leaving some people upset at what they were missing, some annoyed at what they were shown, and many feeling manipulated.

The algorithms that sort content for Facebook and other social media megasites change constantly. The precise formulas they employ at any given moment aren’t publicly known. But one of the key metrics is engagement, such as how many people have commented on a post or what type of emoji reactions it’s received.

As social media platforms like Facebook and Twitter, which shifted its default from chronological to algorithmic feeds in 2016, became more dominant as sources of news and political debate, people began to fear that algorithms were taking control of America’s politics.

Then came the 2016 election. In the wake of Trump’s defeat of Hillary Clinton in the presidential race, reports started trickling out that Russia may have posted on U.S. social media in an attempt to influence election results. Eventually it emerged that employees of a Russian company called the Internet Research Agency had posed as American individuals and groups on Facebook, Instagram, Tumblr, Twitter, and YouTube. These accounts posted and paid for ads on inflammatory topics, criticized candidates (especially Clinton), and sometimes shared fake news. The Senate Select Committee on Intelligence opened an investigation, and Facebook, Google, and Twitter executives were called before Congress to testify.”

“Progressives continued to embrace this explanation with each new and upsetting political development. The alt-right? Blame algorithms! Conspiracy theories about Clinton and sex trafficking? Algorithms! Nice Aunt Sue becoming a cantankerous loon online? Algorithms, of course.

Conservatives learned to loathe the algorithm a little later. Under fire about Russian trolls and other liberal bugaboos, tech companies started cracking down on a widening array of content. Conservatives became convinced that different kinds of algorithms—the ones used to find and deal with hate speech, spam, and other kinds of offensive posts—were more likely to flag and punish conservative voices. They also suspected that algorithms determining what people did see were biased against conservatives.”

“A common thread in all this is the idea that algorithms are powerful engines of personal and political behavior, either deliberately engineered to push us to some predetermined outcome or negligently wielded in spite of clear dangers. Inevitably, this narrative produced legislative proposals”

“It’s no secret that tech companies engineer their platforms to keep people coming back. But this isn’t some uniquely nefarious feature of social media businesses. Keeping people engaged and coming back is the crux of entertainment entities from TV networks to amusement parks.

Moreover, critics have the effect of algorithms precisely backward. A world without algorithms would mean kids (and everyone else) encountering more offensive or questionable content.

Without the news feed algorithm, “the first thing that would happen is that people would see more, not less, hate speech; more, not less, misinformation; more, not less, harmful content,” Nick Clegg, Meta’s then–vice president of global affairs, told George Stephanopoulos last year. That’s because algorithms are used to “identify and deprecate and downgrade bad content.” After all, algorithms are just sorting tools. So Facebook uses them to sort and downgrade hateful content.

“Without [algorithms], you just get an undifferentiated mass of content, and that’s not very useful,” noted Techdirt editor Mike Masnick last March.”

“several studies suggest social media is actually biased toward conservatives. A paper published in Research & Politics in 2022 found that a Facebook algorithm change in 2018 benefitted local Republicans more than local Democrats. In 2021, Twitter looked at how its algorithms amplify political content, examining millions of tweets sent by elected officials in seven countries, as well as “hundreds of millions” of tweets in which people shared links to articles. It found that “in six out of seven countries—all but Germany—Tweets posted by accounts from the political right receive more algorithmic amplification than the political left” and that right-leaning news outlets also “see greater algorithmic amplification.”

As for the Republican email algorithms bill, it would almost certainly backfire. Email services like Gmail use algorithms to sort out massive amounts of spam: If the GOP bill passed, it could mean email users would end up seeing a lot more spam in their inboxes as services strove to avoid liability.”

“it becomes clear why people might feel like algorithms have increased polarization. Life not long ago meant rarely engaging in political discussion with people outside one’s immediate community, where viewpoints tend to coalesce or are glossed over for the sake of propriety. For instance, before Facebook, my sister and an old family friend would likely never have gotten into clashes about Trump—it just wouldn’t have come up in the types of interactions they found themselves in. But that doesn’t mean they’re more politically divergent now; they just know more about it. Far from limiting one’s horizons, engaging with social media means greater exposure to opposing viewpoints, information that challenges one’s beliefs, and sometimes surprising perspectives from people around you.

The evidence used to support the social media/polarization hypothesis is often suspect. For instance, people often point to political polarization. But polarization seems to have started its rise decades before Facebook and Twitter came along.”

“For the average person online, algorithms do a lot of good. They help us get recommendations tailored to our tastes, save time while shopping online, learn about films and music we might not otherwise be exposed to, avoid email spam, keep up with the biggest news from friends and family, and be exposed to opinions we might not otherwise hear.”

“If algorithms are driving political chaos, we don’t have to look at the deeper rot in our democratic systems. If algorithms are driving hate and paranoia, we don’t have to grapple with the fact that racism, misogyny, antisemitism, and false beliefs never faded as much as we thought they had. If the algorithms are causing our troubles, we can pass laws to fix the algorithms. If algorithms are the problem, we don’t have to fix ourselves.

Blaming algorithms allows us to avoid a harder truth. It’s not some mysterious machine mischief that’s doing all of this. It’s people, in all our messy human glory and misery. Algorithms sort for engagement, which means they sort for what moves us, what motivates us to act and react, what generates interest and attention. Algorithms reflect our passions and predilections back at us.”

The Arctic Is Becoming Wetter and Stormier, Scientists Warn

“Even though 2022 was only the Arctic’s sixth warmest year on record, researchers saw plenty of new signs this year of how the region is changing.
A September heat wave in Greenland, for instance, caused the most severe melting of the island’s ice sheet for that time of the year in over four decades of continuous satellite monitoring. In 2021, an August heat wave had caused it to rain at the ice sheet’s summit for the first time.”

“Warming at the top of the Earth raises sea levels worldwide, changes the way heat and water circulate in the oceans, and might even influence extreme weather events like heat waves and rainstorms, scientists say. But Arctic communities feel the impacts first.”

“Between October 2021 and September, air temperatures above Arctic lands were the sixth warmest since 1900, the report card said, noting that the seven warmest years have been the last seven. Rising temperatures have helped plants, shrubs and grasses grow in parts of the Arctic tundra, and 2022 saw levels of green vegetation that were the fourth highest since 2000”