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By the end of World War II, however, American corporations were facing severe talent shortages. Their senior executives were growing old, and a dearth of hiring from the Depression through the war had resulted in a shortfall of able, well-trained managers. Finding people who had the potential to rise quickly through the ranks became an overriding preoccupation of American businesses. They began to devise a formal hiring-and-management system based in part on new studies of human behavior, and in part on military techniques developed during both world wars, when huge mobilization efforts and mass casualties created the need to get the right people into the right roles as efficiently as possible. By the 1950s, it was not unusual for companies to spend days with young applicants for professional jobs, conducting a battery of tests, all with an eye toward corner-office potential.

But companies abandoned their hard-edged practices for another important reason: many of their methods of evaluation turned out not to be very scientific.

this regime, so widespread in corporate America at mid-century, had almost disappeared by 1990. “I think an HR person from the late 1970s would be stunned to see how casually companies hire now,”

Many factors explain the change, he said, and then he ticked off a number of them: Increased job-switching has made it less important and less economical for companies to test so thoroughly. A heightened focus on short-term financial results has led to deep cuts in corporate functions that bear fruit only in the long term. The Civil Rights Act of 1964, which exposed companies to legal liability for discriminatory hiring practices, has made HR departments wary of any broadly applied and clearly scored test that might later be shown to be systematically biased.

about a quarter of the country’s corporations were using similar tests to evaluate managers and junior executives, usually to assess whether they were ready for bigger roles.

He has encouraged the company’s HR executives to think about applying the games to the recruitment and evaluation of all professional workers.

Knack makes app-based video games, among them Dungeon Scrawl, a quest game requiring the player to navigate a maze and solve puzzles, and Wasabi Waiter, which involves delivering the right sushi to the right customer at an increasingly crowded happy hour. These games aren’t just for play: they’ve been designed by a team of neuroscientists, psychologists, and data scientists to suss out human potential. Play one of them for just 20 minutes, says Guy Halfteck, Knack’s founder, and you’ll generate several megabytes of data, exponentially more than what’s collected by the SAT or a personality test. How long you hesitate before taking every action, the sequence of actions you take, how you solve problems—all of these factors and many more are logged as you play, and then are used to analyze your creativity, your persistence, your capacity to learn quickly from mistakes, your ability to prioritize, and even your social intelligence and personality. The end result, Halfteck says, is a high-resolution portrait of your psyche and intellect, and an assessment of your potential as a leader or an innovator.

When the results came back, Haringa recalled, his heart began to beat a little faster. Without ever seeing the ideas, without meeting or interviewing the people who’d proposed them, without knowing their title or background or academic pedigree, Knack’s algorithm had identified the people whose ideas had panned out. The top 10 percent of the idea generators as predicted by Knack were in fact those who’d gone furthest in the process.

What Knack is doing, Haringa told me, “is almost like a paradigm shift.” It offers a way for his GameChanger unit to avoid wasting time on the 80 people out of 100—nearly all of whom look smart, well-trained, and plausible on paper—whose ideas just aren’t likely to work out.

Aptitude, skills, personal history, psychological stability, discretion, loyalty—companies at the time felt they had a need (and the right) to look into them all. That ambit is expanding once again, and this is undeniably unsettling. Should the ideas of scientists be dismissed because of the way they play a game? Should job candidates be ranked by what their Web habits say about them? Should the “data signature” of natural leaders play a role in promotion? These are all live questions today, and they prompt heavy concerns: that we will cede one of the most subtle and human of skills, the evaluation of the gifts and promise of other people, to machines; that the models will get it wrong; that some people will never get a shot in the new workforce.

scoring distance from work could violate equal-employment-opportunity standards. Marital status? Motherhood? Church membership? “Stuff like that,” Meyerle said, “we just don’t touch”—at least not in the U.S., where the legal environment is strict. Meyerle told me that Evolv has looked into these sorts of factors in its work for clients abroad, and that some of them produce “startling results.”

consider the alternative. A mountain of scholarly literature has shown that the intuitive way we now judge professional potential is rife with snap judgments and hidden biases, rooted in our upbringing or in deep neurological connections that doubtless served us well on the savanna but would seem to have less bearing on the world of work.

We may like to think that society has become more enlightened since those days, and in many ways it has, but our biases are mostly unconscious, and they can run surprisingly deep. Consider race. For a 2004 study called “Are Emily and Greg More Employable Than Lakisha and Jamal?,” the economists Sendhil Mullainathan and Marianne Bertrand put white-sounding names (Emily Walsh, Greg Baker) or black-sounding names (Lakisha Washington, Jamal Jones) on similar fictitious résumés, which they then sent out to a variety of companies in Boston and Chicago. To get the same number of callbacks, they learned, they needed to either send out half again as many résumés with black names as those with white names, or add eight extra years of relevant work experience to the résumés with black names.

a sociologist at Northwestern, spent parts of the three years from 2006 to 2008 interviewing professionals from elite investment banks, consultancies, and law firms about how they recruited, interviewed, and evaluated candidates, and concluded that among the most important factors driving their hiring recommendations were—wait for it—shared leisure interests.

Lacking “reliable predictors of future performance,” Rivera writes, “assessors purposefully used their own experiences as models of merit.” Former college athletes “typically prized participation in varsity sports above all other types of involvement.” People who’d majored in engineering gave engineers a leg up, believing they were better prepared.

the prevailing system of hiring and management in this country involves a level of dysfunction that should be inconceivable in an economy as sophisticated as ours. Recent survey data collected by the Corporate Executive Board, for example, indicate that nearly a quarter of all new hires leave their company within a year of their start date, and that hiring managers wish they’d never extended an offer to one out of every five members on their team

In the late 1990s, as these assessments shifted from paper to digital formats and proliferated, data scientists started doing massive tests of what makes for a successful customer-support technician or salesperson. This has unquestionably improved the quality of the workers at many firms.

In 2010, however, Xerox switched to an online evaluation that incorporates personality testing, cognitive-skill assessment, and multiple-choice questions about how the applicant would handle specific scenarios that he or she might encounter on the job. An algorithm behind the evaluation analyzes the responses, along with factual information gleaned from the candidate’s application, and spits out a color-coded rating: red (poor candidate), yellow (middling), or green (hire away). Those candidates who score best, I learned, tend to exhibit a creative but not overly inquisitive personality, and participate in at least one but not more than four social networks, among many other factors. (Previous experience, one of the few criteria that Xerox had explicitly screened for in the past, turns out to have no bearing on either productivity or retention

When Xerox started using the score in its hiring decisions, the quality of its hires immediately improved. The rate of attrition fell by 20 percent in the initial pilot period, and over time, the number of promotions rose. Xerox still interviews all candidates in person before deciding to hire them, Morse told me, but, she added, “We’re getting to the point where some of our hiring managers don’t even want to interview anymore”

Gone are the days, Ostberg told me, when, say, a small survey of college students would be used to predict the statistical validity of an evaluation tool. “We’ve got a data set of 347,000 actual employees who have gone through these different types of assessments or tools,” he told me, “and now we have performance-outcome data, and we can split those and slice and dice by industry and location.”

Evolv’s tests allow companies to capture data about everybody who applies for work, and everybody who gets hired—a complete data set from which sample bias, long a major vexation for industrial-organization psychologists, simply disappears. The sheer number of observations that this approach makes possible allows Evolv to say with precision which attributes matter more to the success of retail-sales workers (decisiveness, spatial orientation, persuasiveness) or customer-service personnel at call centers (rapport-building)

There are some data that Evolv simply won’t use, out of a concern that the information might lead to systematic bias against whole classes of people

the idea that hiring was a science fell out of favor. But now it’s coming back, thanks to new technologies and methods of analysis that are cheaper, faster, and much-wider-ranging than what we had before

what most excites him are the possibilities that arise from monitoring the entire life cycle of a worker at any given company.

Now the two companies are working together to marry pre-hire assessments to an increasing array of post-hire data: about not only performance and duration of service but also who trained the employees; who has managed them; whether they were promoted to a supervisory role, and how quickly; how they performed in that role; and why they eventually left.

What begins with an online screening test for entry-level workers ends with the transformation of nearly every aspect of hiring, performance assessment, and management.

I turned to Sandy Pentland, the director of the Human Dynamics Laboratory at MIT. In recent years, Pentland has pioneered the use of specialized electronic “badges” that transmit data about employees’ interactions as they go about their days. The badges capture all sorts of information about formal and informal conversations: their length; the tone of voice and gestures of the people involved; how much those people talk, listen, and interrupt; the degree to which they demonstrate empathy and extroversion; and more. Each badge generates about 100 data points a minute.

he tried the badges out on about 2,500 people, in 21 different organizations, and learned a number of interesting lessons. About a third of team performance, he discovered, can usually be predicted merely by the number of face-to-face exchanges among team members. (Too many is as much of a problem as too few.) Using data gathered by the badges, he was able to predict which teams would win a business-plan contest, and which workers would (rightly) say they’d had a “productive” or “creative” day. Not only that, but he claimed that his researchers had discovered the “data signature” of natural leaders, whom he called “charismatic connectors” and all of whom, he reported, circulate actively, give their time democratically to others, engage in brief but energetic conversations, and listen at least as much as they talk.

His group is developing apps to allow team members to view their own metrics more or less in real time, so that they can see, relative to the benchmarks of highly successful employees, whether they’re getting out of their offices enough, or listening enough, or spending enough time with people outside their own team.

Torrents of data are routinely collected by American companies and now sit on corporate servers, or in the cloud, awaiting analysis. Bloomberg reportedly logs every keystroke of every employee, along with their comings and goings in the office. The Las Vegas casino Harrah’s tracks the smiles of the card dealers and waitstaff on the floor (its analytics team has quantified the impact of smiling on customer satisfaction). E‑mail, of course, presents an especially rich vein to be mined for insights about our productivity, our treatment of co-workers, our willingness to collaborate or lend a hand, our patterns of written language, and what those patterns reveal about our intelligence, social skills, and behavior.

people analytics will ultimately have a vastly larger impact on the economy than the algorithms that now trade on Wall Street or figure out which ads to show us. He reminded me that we’ve witnessed this kind of transformation before in the history of management science. Near the turn of the 20th century, both Frederick Taylor and Henry Ford famously paced the factory floor with stopwatches, to improve worker efficiency.

“The quantities of data that those earlier generations were working with,” he said, “were infinitesimal compared to what’s available now. There’s been a real sea change in the past five years, where the quantities have just grown so large—petabytes, exabytes, zetta—that you start to be able to do things you never could before.”

People analytics will unquestionably provide many workers with more options and more power. Gild, for example, helps companies find undervalued software programmers, working indirectly to raise those people’s pay. Other companies are doing similar work. One called Entelo, for instance, specializes in using algorithms to identify potentially unhappy programmers who might be receptive to a phone cal

He sees it not only as a boon to a business’s productivity and overall health but also as an important new tool that individual employees can use for self-improvement: a sort of radically expanded The 7 Habits of Highly Effective People, custom-written for each of us, or at least each type of job, in the workforce.

the most exotic development in people analytics today is the creation of algorithms to assess the potential of all workers, across all companies, all the time.

The way Gild arrives at these scores is not simple. The company’s algorithms begin by scouring the Web for any and all open-source code, and for the coders who wrote it. They evaluate the code for its simplicity, elegance, documentation, and several other factors, including the frequency with which it’s been adopted by other programmers. For code that was written for paid projects, they look at completion times and other measures of productivity. Then they look at questions and answers on social forums such as Stack Overflow, a popular destination for programmers seeking advice on challenging projects. They consider how popular a given coder’s advice is, and how widely that advice ranges.

The algorithms go further still. They assess the way coders use language on social networks from LinkedIn to Twitter; the company has determined that certain phrases and words used in association with one another can distinguish expert programmers from less skilled ones. Gild knows these phrases and words are associated with good coding because it can correlate them with its evaluation of open-source code, and with the language and online behavior of programmers in good positions at prestigious companies.

having made those correlations, Gild can then score programmers who haven’t written open-source code at all, by analyzing the host of clues embedded in their online histories. They’re not all obvious, or easy to explain. Vivienne Ming, Gild’s chief scientist, told me that one solid predictor of strong coding is an affinity for a particular Japanese manga site.

Gild’s CEO, Sheeroy Desai, told me he believes his company’s approach can be applied to any occupation characterized by large, active online communities, where people post and cite individual work, ask and answer professional questions, and get feedback on projects. Graphic design is one field that the company is now looking at, and many scientific, technical, and engineering roles might also fit the bill. Regardless of their occupation, most people leave “data exhaust” in their wake, a kind of digital aura that can reveal a lot about a potential hire.

professionally relevant personality traits can be judged effectively merely by scanning Facebook feeds and photos. LinkedIn, of course, captures an enormous amount of professional data and network information, across just about every profession. A controversial start-up called Klout has made its mission the measurement and public scoring of people’s online social influence.

Mullainathan expressed amazement at how little most creative and professional workers (himself included) know about what makes them effective or ineffective in the office. Most of us can’t even say with any certainty how long we’ve spent gathering information for a given project, or our pattern of information-gathering, never mind know which parts of the pattern should be reinforced, and which jettisoned. As Mullainathan put it, we don’t know our own “production function.”

Over time, better job-matching technologies are likely to begin serving people directly, helping them see more clearly which jobs might suit them and which companies could use their skills. In the future, Gild plans to let programmers see their own profiles and take skills challenges to try to improve their scores. It intends to show them its estimates of their market value, too, and to recommend coursework that might allow them to raise their scores even more. Not least, it plans to make accessible the scores of typical hires at specific companies, so that software engineers can better see the profile they’d need to land a particular job

Knack, for its part, is making some of its video games available to anyone with a smartphone, so people can get a better sense of their strengths, and of the fields in which their strengths would be most valued. (Palo Alto High School recently adopted the games to help students assess careers.) Ultimately, the company hopes to act as matchmaker between a large network of people who play its games (or have ever played its games) and a widening roster of corporate clients, each with its own specific profile for any given type of job.

When I began my reporting for this story, I was worried that people analytics, if it worked at all, would only widen the divergent arcs of our professional lives, further gilding the path of the meritocratic elite from cradle to grave, and shutting out some workers more definitively. But I now believe the opposite is likely to happen, and that we’re headed toward a labor market that’s fairer to people at every stage of their careers

For decades, as we’ve assessed people’s potential in the professional workforce, the most important piece of data—the one that launches careers or keeps them grounded—has been educational background: typically, whether and where people went to college, and how they did there. Over the past couple of generations, colleges and universities have become the gatekeepers to a prosperous life. A degree has become a signal of intelligence and conscientiousness, one that grows stronger the more selective the school and the higher a student’s GPA, that is easily understood by employers, and that, until the advent of people analytics, was probably unrivaled in its predictive powers.

the limitations of that signal—the way it degrades with age, its overall imprecision, its many inherent biases, its extraordinary cost—are obvious. “Academic environments are artificial environments,” Laszlo Bock, Google’s senior vice president of people operations, told The New York Times in June. “People who succeed there are sort of finely trained, they’re conditioned to succeed in that environment,” which is often quite different from the workplace.

because one’s college history is such a crucial signal in our labor market, perfectly able people who simply couldn’t sit still in a classroom at the age of 16, or who didn’t have their act together at 18, or who chose not to go to graduate school at 22, routinely get left behind for good. That such early factors so profoundly affect career arcs and hiring decisions made two or three decades later is, on its face, absurd.

I spoke with managers at a lot of companies who are using advanced analytics to reevaluate and reshape their hiring, and nearly all of them told me that their research is leading them toward pools of candidates who didn’t attend college—for tech jobs, for high-end sales positions, for some managerial roles. In some limited cases, this is because their analytics revealed no benefit whatsoever to hiring people with college degrees; in other cases, and more often, it’s because they revealed signals that function far better than college history,

Google, too, is hiring a growing number of nongraduates. Many of the people I talked with reported that when it comes to high-paying and fast-track jobs, they’re reducing their preference for Ivy Leaguers and graduates of other highly selective schools.

This process is just beginning. Online courses are proliferating, and so are online markets that involve crowd-sourcing. Both arenas offer new opportunities for workers to build skills and showcase competence. Neither produces the kind of instantly recognizable signals of potential that a degree from a selective college, or a first job at a prestigious firm, might. That’s a problem for traditional hiring managers, because sifting through lots of small signals is so difficult and time-consuming.

all of these new developments raise philosophical questions. As professional performance becomes easier to measure and see, will we become slaves to our own status and potential, ever-focused on the metrics that tell us how and whether we are measuring up? Will too much knowledge about our limitations hinder achievement and stifle our dreams? All I can offer in response to these questions, ironically, is my own gut sense, which leads me to feel cautiously optimistic.

Google’s understanding of the promise of analytics is probably better than anybody else’s, and the company has been changing its hiring and management practices as a result of its ongoing analyses. (Brainteasers are no longer used in interviews, because they do not correlate with job success; GPA is not considered for anyone more than two years out of school, for the same reason—the list goes on.) But for all of Google’s technological enthusiasm, these same practices are still deeply human. A real, live person looks at every résumé the company receives. Hiring decisions are made by committee and are based in no small part on opinions formed during structured interviews.

Perhaps Fisher offers the nation an opportunity to divorce, once and for all, memory from history. History may be an attempt to memorialize and preserve the past, but it is not memory; memories can serve as primary sources, but they do not stand alone as history. A history is essentially a collection of memories, analyzed and reduced into meaningful conclusions—but that collection depends on the memories chosen.

Memories make for a risky foundation: As events recede further into the past, the facts are distorted or augmented by entirely new details

people construct unique memories while informing perfectly valid histories. Just as there is a plurality of memories, so, too, is there a plurality of histories.

Scholars who read a diverse set of historians who are all focused on the same specific period or event are engaging in historiography

This approach exposes textbooks as nothing more than a compilation of histories that the authors deemed to be most relevant and useful.

In historiography, the barrier between historian and student is dropped, exposing a conflict-ridden landscape. A diplomatic historian approaches an event from the perspective of the most influential statesmen (who are most often white males), analyzing the context, motives, and consequences of their decisions. A cultural historian peels back the objects, sights, and sounds of a period to uncover humanity’s underlying emotions and anxieties. A Marxist historian adopts the lens of class conflict to explain the progression of events. There are intellectual historians, social historians, and gender historians, among many others. Historians studying the same topic will draw different interpretations—sometimes radically so, depending on the sources they draw from

Jacoba Urist points out that history is "about explaining and interpreting past events analytically." If students are really to learn and master these analytical tools, then it is absolutely essential that they read a diverse set of historians and learn how brilliant men and women who are scrutinizing the same topic can reach different conclusions

Rather than constructing a curriculum based on the muddled consensus of boards, legislatures, and think tanks, schools should teach students history through historiography. The shortcomings of one historian become apparent after reading the work of another one on the list.

Although, as Urist notes, the AP course is "designed to teach students to think like historians," my own experience in that class suggests that it fails to achieve that goal.

The course’s framework has always served as an outline of important concepts aiming to allow educators flexibility in how to teach; it makes no reference to historiographical conflicts. Historiography was an epiphany for me because I had never before come face-to-face with how historians think and reason

When I took AP U.S. History, I jumbled these diverse histories into one indistinct narrative. Although the test involved open-ended essay questions, I was taught that graders were looking for a firm thesis—forcing students to adopt a side. The AP test also, unsurprisingly, rewards students who cite a wealth of supporting details

By the time I took the test in 2009, I was a master at "checking boxes," weighing political factors equally against those involving socioeconomics and ensuring that previously neglected populations like women and ethnic minorities received their due. I did not know that I was pulling ideas from different historiographical traditions. I still subscribed to the idea of a prevailing national narrative and served as an unwitting sponsor of synthesis, oblivious to the academic battles that made such synthesis impossible.

Although there may be an inclination to seek to establish order where there is chaos, that urge must be resisted in teaching history. Public controversies over memory are hardly new. Students must be prepared to confront divisiveness, not conditioned to shoehorn agreement into situations where none is possible

When conflict is accepted rather than resisted, it becomes possible for different conceptions of American history to co-exist. There is no longer a need to appoint a victor.

More importantly, the historiographical approach avoids pursuing truth for the sake of satisfying a national myth

The country’s founding fathers crafted some of the finest expressions of personal liberty and representative government the world has ever seen; many of them also held fellow humans in bondage. This paradox is only a problem if the goal is to view the founding fathers as faultless, perfect individuals. If multiple histories are embraced, no one needs to fear that one history will be lost.

History is not indoctrination. It is a wrestling match. For too long, the emphasis has been on pinning the opponent. It is time to shift the focus to the struggle itself

There is no better way to use the past to inform the present than by accepting the impossibility of a definitive history—and by ensuring that current students are equipped to grapple with the contested memories in their midst.

Search engine companies, like most online services, make money not only by selling ads but also by tracking users and selling their data through real-time bidding on it. People are often led to misinformation by their desire for sensational and entertaining news as well as information that is either controversial or confirms their views.

This pattern of thrilling and unverified stories emerging and people clicking on them continues, with people apparently either being unconcerned with the truth or believing that if a trusted service such as Google Search is showing these stories to them then the stories must be true. More recently, a disproven report claiming China let the coronavirus leak from a lab gained traction on search engines because of this vicious cycle.

“Today’s action will allow us to review the matter and ensure the Interior Department is upholding its trust and treaty obligations in accordance with the law.”

Fossil fuel companies have begun using horizontal drilling techniques to access oil and gas resources below the river and Lake Sakakawea, a 200-mile-long reservoir created with the construction of Garrison Dam in the 1950s that permanently flooded one-quarter of the Fort Berthold Reservation and forced 90% of tribal members to relocate.

$100 billion in oil and gas royalties and future payments are waiting to be claimed, and the state has pushed for control of mineral rights on the reservation section of the river.

“Structural racism is an unfortunate term,” said Dr. Livingston, who is white. “Personally, I think taking racism out of the conversation will help

Many people like myself are offended by the implication that we are somehow racist.”

The podcast was promoted with a tweet from the journal that said, “No physician is racist, so how can there be structural racism in health care?”

The response to both was swift and angry, prompting the journal to take down the podcast and delete the tweet.

Comments made in the podcast were inaccurate, offensive, hurtful, and inconsistent with the standards of JAMA,

The A.M.A.’s email to employees promised that the investigation would probe “

“We are instituting changes that will address and prevent such failures from happening again.”

“It’s not just that this podcast is problematic — it’s that there is a long and documented history of institutional racism at JAMA,”

“That podcast should never have happened,”

The fact that podcast was conceived of, recorded and posted was unconscionable.”

“I think it caused an incalculable amount of pain and trauma to Black physicians and patients,” she said. “And I think it’s going to take a long time for the journal to heal that pain.

“staff and leadership are overwhelmingly white and economically privileged,” and he committed to reviewing its editorial process.

Dr. Livingston later resigned.

how the podcast and associated tweet were developed, reviewed, and ultimately published,” and said that the association had engaged independent investigators to ensure objectivity.

The email did not offer a date for conclusion of the investigation.

According to the NFL, a panel of neuropsychologists that was formed recently to propose a new testing regime to the court includes two female and three Black doctors.

The NFL noted that the norms were developed in medicine “to stop bias in testing, not perpetrate it.”

The binary race norms, when they are used in the testing, assumes that Black patients start with worse cognitive function than whites and other non-Blacks.

The awards so far have averaged $516,000 for the 379 players with early-stage dementia and $715,000 for the 207 players with moderate dementia.

he settlement ended thousands of lawsuits that accused the NFL of long hiding what it knew about the link between concussions and traumatic brain injury.

The quality of mathematics research in the United States today is the envy of the scientific world. This is a direct result of the openness and inclusivity of the profession.

Can Americans maintain this unmatched excellence in the future? There are worrisome signs that suggest not.

The Organization for Economic Cooperation and Development compares mathematical proficiency among 15-year-olds by country worldwide. According to its 2018 report, America ranked 37th while China, America’s main competitor for world leadership, came in first.

This is despite the fact that the United States is the fifth-highest spender per pupil among the 37 developed OECD nations

This massive failure of our K-12 education system trickles through the STEM pipeline.

At the undergraduate level, too few American students are prepared for higher-level mathematics courses. These students are then unprepared for rigorous graduate-level work

According to our own experiences at the universities where we teach, an overwhelming majority of American students with strong math backgrounds are either foreign-born or first-generation students who have additional support from their education-conscious families. At all levels, STEM disciplines are more and more dependent on a constant flow of foreign talent.

There are many reasons for this failure, but the way that we educate and prepare teachers is particularly influential. The vast majority of K-12 math teachers are graduates of teacher-preparation programs that teach very little substantive mathematics

This has led to a constant stream of ill-advised and dumbed-down reforms. One of the latest fads is anti-racist mathematics. Promoted in several states, the bizarre doctrine threatens to further degrade the teaching of mathematics.

Another major concern is the twisted interpretation of diversity, equity, and inclusion (DEI).

Under the banner of DEI, universities are abandoning the use of standardized tests like the SAT and GRE in admissions, and cities are considering scrapping academic tracking and various gifted programs in schools, which they deem “inequitable.”

such programs are particularly effective, when properly implemented, at discovering and encouraging talented children from disadvantaged backgrounds.

The new 2021 Mathematics Framework, currently under consideration by California’s Department of Education, does away “with all tracking, acceleration, gifted programs, or any instruction that involves clustering by individual differences, without expressing any awareness of the impact these drastic alterations would have in preparing STEM-ready candidates.”

These measures will not only hinder the progress of the generations of our future STEM workforce but also contribute to structural inequalities, as they are uniquely detrimental to students whose parents cannot send them to private schools or effective enrichment programs.

These are just a few examples of an unprecedented fervor for revolutionary change in the name of Critical Race Theory (CRT), a doctrine that views the world as a fierce battleground for the narratives of various identity groups.

This will only lead to a further widening of racial disparities in educational outcomes while lowering American children’s rankings in education internationally.

Ill-conceived DEI policies, often informed by CRT, and the declining standards of K-12 math education feed each other in a vicious circle

Regarding minorities, in particular, public K-12 education all too often produces students unprepared to compete, thus leading to large disparities in admissions at universities, graduate programs, and faculty positions. This disparity is then condemned as a manifestation of structural racism, resulting in administrative measures to lower the evaluation criteria. Lowering standards at all levels leads eventually to even worse outcomes and larger disparities, and so on in a downward spiral.

A case in point is the recent report by the American Mathematical Society that accuses the entire mathematics community, with the thinnest of specific evidence, of systemic racial discrimination. A major justification put forward for such a grave accusation is the lack of sufficient representation of Black mathematicians in the professio

the report, while raising awareness of several ugly facts from the long-ago past, makes little effort to address the real reasons for this, mainly the catastrophic failure of the K-12 mathematical educational system.

The National Science Foundation, a federal institution meant to support fundamental research, is now diverting some of its limited funding to various DEI initiatives of questionable benefit.

Meanwhile, other countries, especially China, are doing precisely the opposite, following the model of our past dedication to objective measures of excellence. How long before we will see a reverse exodus, away from the United States?

The present crisis can still be reversed by focusing on a few concrete actions:

Improve schools in urban areas and inner-city neighborhoods by following the most promising education programs. These programs demonstrate that inner-city children benefit if they are challenged by high standards and a nurturing environment.

Follow the lead of other highly successful rigorous programs such as BASIS schools and Math for America, which focus on rigorous STEM curricula, combined with 21st-century teaching methods, and recruit talented teachers to help them build on their STEM knowledge and teaching methods.

Increase, rather than eliminate, tailored instruction, both for accelerated and remedial math courses.

Reject the soft bigotry of low expectations, that Black children cannot do well in competitive mathematics programs and need dumbed-down ethnocentric versions of mathematics.

Uphold the objective selection process based on merit at all levels of education and research.

The Human Rights Campaign calls the slew of legislation restricting the access of trans children to sports "unprecedented," grouping it in with other bills introduced recently in states like South Carolina and Texas that would limit certain kinds of medical treatments for trans youth.

Already this year, state legislators have introduced at least 35 bills aimed at restricting trans girls and women from playing on girls' and women's sports teams, according to the LGBTQ advocacy group Freedom for All Americans. That's up from only two in 2019.

"Transgender kids are kids," the organization said. "Excluding and discriminating against them does great harm to them and it weakens the communities in which these children feel excluded and marginalized."

The search for linguistic universals yielded interesting data on languages, but after decades of work, not a single proposed universal has withstood scrutiny. Instead, as linguists probed deeper into the world's languages (7,000 or so, only a fraction of them analyzed), innumerable unpredictable differences emerged.

In the past decade, cognitive scientists have begun to measure not just how people talk, but also how they think, asking whether our understanding of even such fundamental domains of experience as space, time and causality could be constructed by language.

About a third of the world's languages (spoken in all kinds of physical environments) rely on absolute directions for space.

As a result of this constant linguistic training, speakers of such languages are remarkably good at staying oriented and keeping track of where they are, even in unfamiliar landscapes.

People rely on their spatial knowledge to build many other more complex or abstract representations including time, number, musical pitch, kinship relations, morality and emotions.

And many other ways to organize time exist in the world's languages. In Mandarin, the future can be below and the past above. In Aymara, spoken in South America, the future is behind and the past in front.

Beyond space, time and causality, patterns in language have been shown to shape many other domains of thought. Russian speakers, who make an extra distinction between light and dark blues in their language, are better able to visually discriminate shades of blue.

Patterns in language offer a window on a culture's dispositions and priorities.

Languages, of course, are human creations, tools we invent and hone to suit our needs

Simply showing that speakers of different languages think differently doesn't tell us whether it's language that shapes thought or the other way around. To demonstrate the causal role of language, what's needed are studies that directly manipulate language and look for effects in cognition.

It marked the first time in the 231-year history of the United States that a president has been impeached twice in his term.

Another presidential run in 2024 would be a very real possibility.Instead, Trump is on the ropes. He's been silenced from social media, including his beloved Twitter account. Even if he is not prohibited from running for office by a Senate conviction, his power and influence within the Republican Party has been dented.

For five years, Trump has been proving critics and prognosticators who penned his political epitaph wrong. He's survived scandals and controversies that would take down most other politicians.This time, at long last, could be different.

. "I believe that Qatar has been judged by the court of perception very early on."

Unions are a prominent presence at Amazon in Europe, but for years, the company successfully fought off labor organizing efforts in the United States.

"The onsite voting proposal, which is in the best interest of all parties — associate convenience, vote fidelity, and timeliness of vote count — was not accepted," Knox said. "We will continue to insist on measures for a fair election, and we want everyone to vote, so our focus is ensuring that's possible."

The workers' union-backed website calls for changes to procedures in disciplining, dismissals and safety.

That final season with the Buckeyes also was one with controversy. Meyer was suspended for three games without pay in the wake of an investigation into what he did about spousal abuse allegations against former assistant coach Zach Smith, the university said.

In states across the country, demand for the vaccine has far outweighed supply

Tens of thousands of people have already received the vaccines, and none of them have reported any serious health problems.

Other states have decided to remain focused exclusively on vaccinating health care workers and nursing home residents before opening up to a wider swath of the public.

Life will return to normal only when society as a whole gains enough protection against the coronavirus. Once countries authorize a vaccine, they’ll only be able to vaccinate a few percent of their citizens at most in the first couple months.

At least 28 states and Washington, D.C., have begun vaccinating older people, a New York Times survey shows, in many cases marking a shift in earlier plans that had put medical workers and nursing home residents at the front of the line for Covid-19 inoculations.

“I couldn’t hug her,” Ms. Johnson said. “We didn’t share food. We each brought our own sandwich. It was just surreal.”

In school, one learns about “the scientific method”—usually a straightforward set of steps, along the lines of “ask a question, propose a hypothesis, perform an experiment, analyze the results.”

That method works in the classroom, where students are basically told what questions to pursue. But real scientists must come up with their own questions, finding new routes through a much vaster landscape.

Since science began, there has been disagreement about how those routes are charted. Two twentieth-century philosophers of science, Karl Popper and Thomas Kuhn, are widely held to have offered the best accounts of this process.

For Popper, Strevens writes, “scientific inquiry is essentially a process of disproof, and scientists are the disprovers, the debunkers, the destroyers.” Kuhn’s scientists, by contrast, are faddish true believers who promulgate received wisdom until they are forced to attempt a “paradigm shift”—a painful rethinking of their basic assumptions.

Working scientists tend to prefer Popper to Kuhn. But Strevens thinks that both theorists failed to capture what makes science historically distinctive and singularly effective.

Sometimes they seek to falsify theories, sometimes to prove them; sometimes they’re informed by preëxisting or contextual views, and at other times they try to rule narrowly, based on t

Why do scientists agree to this scheme? Why do some of the world’s most intelligent people sign on for a lifetime of pipetting?

Strevens thinks that they do it because they have no choice. They are constrained by a central regulation that governs science, which he calls the “iron rule of explanation.” The rule is simple: it tells scientists that, “if they are to participate in the scientific enterprise, they must uncover or generate new evidence to argue with”; from there, they must “conduct all disputes with reference to empirical evidence alone.”

, it is “the key to science’s success,” because it “channels hope, anger, envy, ambition, resentment—all the fires fuming in the human heart—to one end: the production of empirical evidence.”

Strevens arrives at the idea of the iron rule in a Popperian way: by disproving the other theories about how scientific knowledge is created.

The problem isn’t that Popper and Kuhn are completely wrong. It’s that scientists, as a group, don’t pursue any single intellectual strategy consistently.

Exploring a number of case studies—including the controversies over continental drift, spontaneous generation, and the theory of relativity—Strevens shows scientists exerting themselves intellectually in a variety of ways, as smart, ambitious people usually do.

“Science is boring,” Strevens writes. “Readers of popular science see the 1 percent: the intriguing phenomena, the provocative theories, the dramatic experimental refutations or verifications.” But, he says,behind these achievements . . . are long hours, days, months of tedious laboratory labor. The single greatest obstacle to successful science is the difficulty of persuading brilliant minds to give up the intellectual pleasures of continual speculation and debate, theorizing and arguing, and to turn instead to a life consisting almost entirely of the production of experimental data.

Ultimately, in fact, it was good that the geologists had a “splendid variety” of somewhat arbitrary opinions: progress in science requires partisans, because only they have “the motivation to perform years or even decades of necessary experimental work.” It’s just that these partisans must channel their energies into empirical observation. The iron rule, Strevens writes, “has a valuable by-product, and that by-product is data.”

Science is often described as “self-correcting”: it’s said that bad data and wrong conclusions are rooted out by other scientists, who present contrary findings. But Strevens thinks that the iron rule is often more important than overt correction.

Eddington was never really refuted. Other astronomers, driven by the iron rule, were already planning their own studies, and “the great preponderance of the resulting measurements fit Einsteinian physics better than Newtonian physics.” It’s partly by generating data on such a vast scale, Strevens argues, that the iron rule can power science’s knowledge machine: “Opinions converge not because bad data is corrected but because it is swamped.”

Why did the iron rule emerge when it did? Strevens takes us back to the Thirty Years’ War, which concluded with the Peace of Westphalia, in 1648. The war weakened religious loyalties and strengthened national ones.

Two regimes arose: in the spiritual realm, the will of God held sway, while in the civic one the decrees of the state were paramount. As Isaac Newton wrote, “The laws of God & the laws of man are to be kept distinct.” These new, “nonoverlapping spheres of obligation,” Strevens argues, were what made it possible to imagine the iron rule. The rule simply proposed the creation of a third sphere: in addition to God and state, there would now be science.

Strevens imagines how, to someone in Descartes’s time, the iron rule would have seemed “unreasonably closed-minded.” Since ancient Greece, it had been obvious that the best thinking was cross-disciplinary, capable of knitting together “poetry, music, drama, philosophy, democracy, mathematics,” and other elevating human disciplines.

We’re still accustomed to the idea that a truly flourishing intellect is a well-rounded one. And, by this standard, Strevens says, the iron rule looks like “an irrational way to inquire into the underlying structure of things”; it seems to demand the upsetting “suppression of human nature.”

Descartes, in short, would have had good reasons for resisting a law that narrowed the grounds of disputation, or that encouraged what Strevens describes as “doing rather than thinking.”

In fact, the iron rule offered scientists a more supple vision of progress. Before its arrival, intellectual life was conducted in grand gestures.

Descartes’s book was meant to be a complete overhaul of what had preceded it; its fate, had science not arisen, would have been replacement by some equally expansive system. The iron rule broke that pattern.

by authorizing what Strevens calls “shallow explanation,” the iron rule offered an empirical bridge across a conceptual chasm. Work could continue, and understanding could be acquired on the other side. In this way, shallowness was actually more powerful than depth.

it also changed what counted as progress. In the past, a theory about the world was deemed valid when it was complete—when God, light, muscles, plants, and the planets cohered. The iron rule allowed scientists to step away from the quest for completeness.

The consequences of this shift would become apparent only with time

In 1713, Isaac Newton appended a postscript to the second edition of his “Principia,” the treatise in which he first laid out the three laws of motion and the theory of universal gravitation. “I have not as yet been able to deduce from phenomena the reason for these properties of gravity, and I do not feign hypotheses,” he wrote. “It is enough that gravity really exists and acts according to the laws that we have set forth.”

What mattered, to Newton and his contemporaries, was his theory’s empirical, predictive power—that it was “sufficient to explain all the motions of the heavenly bodies and of our sea.”

Descartes would have found this attitude ridiculous. He had been playing a deep game—trying to explain, at a fundamental level, how the universe fit together. Newton, by those lights, had failed to explain anything: he himself admitted that he had no sense of how gravity did its work

Strevens sees its earliest expression in Francis Bacon’s “The New Organon,” a foundational text of the Scientific Revolution, published in 1620. Bacon argued that thinkers must set aside their “idols,” relying, instead, only on evidence they could verify. This dictum gave scientists a new way of responding to one another’s work: gathering data.

Quantum theory—which tells us that subatomic particles can be “entangled” across vast distances, and in multiple places at the same time—makes intuitive sense to pretty much nobody.

Without the iron rule, Strevens writes, physicists confronted with such a theory would have found themselves at an impasse. They would have argued endlessly about quantum metaphysics.

ollowing the iron rule, they can make progress empirically even though they are uncertain conceptually. Individual researchers still passionately disagree about what quantum theory means. But that hasn’t stopped them from using it for practical purposes—computer chips, MRI machines, G.P.S. networks, and other technologies rely on quantum physics.

One group of theorists, the rationalists, has argued that science is a new way of thinking, and that the scientist is a new kind of thinker—dispassionate to an uncommon degree.

As evidence against this view, another group, the subjectivists, points out that scientists are as hopelessly biased as the rest of us. To this group, the aloofness of science is a smoke screen behind which the inevitable emotions and ideologies hide.

At least in science, Strevens tells us, “the appearance of objectivity” has turned out to be “as important as the real thing.”

The subjectivists are right, he admits, inasmuch as scientists are regular people with a “need to win” and a “determination to come out on top.”

But they are wrong to think that subjectivity compromises the scientific enterprise. On the contrary, once subjectivity is channelled by the iron rule, it becomes a vital component of the knowledge machine. It’s this redirected subjectivity—to come out on top, you must follow the iron rule!—that solves science’s “problem of motivation,” giving scientists no choice but “to pursue a single experiment relentlessly, to the last measurable digit, when that digit might be quite meaningless.”

If it really was a speech code that instigated “the extraordinary attention to process and detail that makes science the supreme discriminator and destroyer of false ideas,” then the peculiar rigidity of scientific writing—Strevens describes it as “sterilized”—isn’t a symptom of the scientific mind-set but its cause.

The iron rule—“a kind of speech code”—simply created a new way of communicating, and it’s this new way of communicating that created science.

Other theorists have explained science by charting a sweeping revolution in the human mind; inevitably, they’ve become mired in a long-running debate about how objective scientists really are

In “The Knowledge Machine: How Irrationality Created Modern Science” (Liveright), Michael Strevens, a philosopher at New York University, aims to identify that special something. Strevens is a philosopher of science

Compared with the theories proposed by Popper and Kuhn, Strevens’s rule can feel obvious and underpowered. That’s because it isn’t intellectual but procedural. “The iron rule is focused not on what scientists think,” he writes, “but on what arguments they can make in their official communications.”

Like everybody else, scientists view questions through the lenses of taste, personality, affiliation, and experience

geologists had a professional obligation to take sides. Europeans, Strevens reports, tended to back Wegener, who was German, while scholars in the United States often preferred Simpson, who was American. Outsiders to the field were often more receptive to the concept of continental drift than established scientists, who considered its incompleteness a fatal flaw.

Strevens’s point isn’t that these scientists were doing anything wrong. If they had biases and perspectives, he writes, “that’s how human thinking works.”

Eddington’s observations were expected to either confirm or falsify Einstein’s theory of general relativity, which predicted that the sun’s gravity would bend the path of light, subtly shifting the stellar pattern. For reasons having to do with weather and equipment, the evidence collected by Eddington—and by his colleague Frank Dyson, who had taken similar photographs in Sobral, Brazil—was inconclusive; some of their images were blurry, and so failed to resolve the matter definitively.

it was only natural for intelligent people who were free of the rule’s strictures to attempt a kind of holistic, systematic inquiry that was, in many ways, more demanding. It never occurred to them to ask if they might illuminate more collectively by thinking about less individually.

In the single-sphered, pre-scientific world, thinkers tended to inquire into everything at once. Often, they arrived at conclusions about nature that were fascinating, visionary, and wrong.

How Does Science Really Work?Science is objective. Scientists are not. Can an “iron rule” explain how they’ve changed the world anyway?By Joshua RothmanSeptember 28, 2020

Many American readers, though, didn’t want a non-hierarchical assessment of their language. They wanted to know which usages were “correct,” because being able to rely on a dictionary to tell you how to sound educated and upper class made becoming upper class seem as if it might be possible. That’s why the public responded badly to Webster’s latest: They craved guidance and rules.

Webster’s Third so unnerved critics and customers because the American idea of social mobility is limited, provisional, and full of paradoxes

There’s no such thing as social mobility if everyone can enjoy it. To be allowed to move around within a hierarchy implies that the hierarchy must be left largely intact. But in America, people have generally accepted the idea of inherited upper-class status, while seeing upward social mobility as something that must be earned.

In a 2001 Harper’s essay about the Webster’s Third controversy, David Foster Wallace called the publication of the dictionary “the Fort Sumter of the contemporary usage wars.”

for decades after the publication of Webster’s Third, people still had intense opinions about dictionaries. In the 1990s, an elderly copy editor once told me, with considerable vehemence, that Merriam-Webster’s Dictionaries were “garbage.” She would only use Houghton Mifflin’s American Heritage Dictionary, which boasted a Usage Panel of experts to advise readers about the finer points of English grammar

what descriptivists do: They describe rather than judge. Nowadays, this approach to dictionary making is generally not contested or even really discussed.

In his 2009 book Going Nucular, Geoffrey Nunberg observes that we now live in a culture in which there are no clear distinctions between highbrow, middlebrow, and lowbrow culture. It stands to reason that in a society in which speaking in a recognizably “highbrow” way confers no benefits, dictionaries will likely matter less

If American Heritage was aggressively branding itself in the 1960s, Merriam-Webster is doing the same now.

The company has a feisty blog and Twitter feed that it uses to criticize linguistic and grammatical choices. President Trump and his administration are regular catalysts for social-media clarifications by Merriam-Webster. The company seems bothered when Trump and his associates change the meanings of words for their own convenience, or when they debase the language more generally.

it seems that the way the company has regained its relevance in the post-print era is by having a strong opinions about how people should use English.

It may be that in spite of Webster’s Third’s noble intentions, language may just be too human a thing to be treated in an entirely detached, scientific way. Indeed, I’m not sure I want to live in a society in which citizens can’t call out government leaders when they start subverting language in distressing ways.

fake news is arguably less dangerous than ultra-partisan sites, which tend to rely on confirming prejudices, spinning stories well beyond the bounds of normal journalistic practice, and wearing partisan leanings on their sleeves

Social media is the platform where the war against misleading news is being fought—and it should be the primary focus for these changes, with increased prominence given to trusted outlets, and toning down or turning off the ability for untrustworthy sources to be liked or shared.

Experts have managed to turn people off all sorts of foods by convincing them it had made them ill when they were a child

“People have a life story, particularly as they get older and for some people it needs to stretch back to the very early stage of life,”

The prevailing account of how we come to believe and remember things is based around the concept of source monitoring. “Every time a thought comes to mind we have to make a decision – have we experienced it [an event], imagined it or have we talked about it with other people,” says Kimberley Wade

Most of the time we make that decision correctly and can identify where these mental experiences come from, but sometimes we get it wrong.

Wade admits she has spent a lot of time recalling an event that was actually something her brother experienced rather than herself, but despite this, it is rich in detail and provokes emotion

Memory researchers have shown it is possible to induce fictional autobiographical memories in volunteers, including accounts of getting lost in a shopping mall and even having tea with a member of the Royal Family

Based on my research, everybody is capable of forming complex false memories, given the right circumstances – Julia Shaw

In some situations, such as after looking at pictures or a video, children are more susceptible to forming false memories than adults. People with certain personality types are also thought to be more prone.

But carrying around false memories from your childhood could be having a far greater impact on you than you may realise too. The events, emotions and experiences we remember from our early years can help to shape who we are as adults, determining our likes, dislikes, fears and even our behaviour.

Memories before the age of three are more than likely to be false. Any that appear very fluid and detailed, as if you were playing back a home video and experiencing a chronological account of a memory, could well also be made up. It is more likely that fuzzy fragments, or snapshots of moments are real, as long as they are not from too early in your life.

We crave a cohesive narrative of our own existence and will even invent stories to give us a more complete picture

Interestingly, scientists have also found positive suggestions, such as “you loved asparagus the first time you ate it” tend to be more effective than negative suggestions like “you got sick drinking vodka”

“Miscarriage of justice, incarceration, loss of reputation, job and status, and family breakdown occur,

One of the major problems with legal cases involving false memories, is that it is currently impossible to distinguish between true and fictional recollections

Efforts have been made to analyse minor false memories in a brain scanner (fMRI) and detect different neurological patterns, but there is nothing as yet to indicate that this technology can be used to detect whether recollections have become distorted.

the most extreme case of memory implantation involves a controversial technique called “regression therapy”, where patients confront childhood traumas, supposedly buried in their subconscious

“Memories are malleable and tend to change slightly each time we revisit them, in the same way that spoken stories do,”

“Therefore at each recollection, new elements can easily be integrated while existing elements can be altered or lost.”

This is not to say that all evidence that relies on memory should be discarded or regarded as unreliable – they often provide the most compelling testimony in criminal cases. But it has led to rules and guidelines about how witnesses and victims should be questioned to ensure their recollections of an event or perpetrator are not contaminated by investigators or prosecutors.

Any memories that appear very fluid and detailed, as if you were playing back a home video, could well also be made up

While this may seem like a bit of fun, many scientists believe the “false memory diet” could be used to tackle obesity and encourage people to reach for healthier options like asparagus, or even help cut people’s alcohol consumption.

Children are more susceptible to forming false memories than adults, especially after looking at photographs or films

And we may not want to rid ourselves of these memories. Our memories, whether fictional or not, can help to bring us closer together.