LinkedIn combed through 259 million profiles and ranked the most commonly used words among 20 countries. Number one was responsible, which was twice was popular as any other word on the list. Here are the top ten:
1. Responsible2. Strategic
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People tend to have one of three beliefs about the meaning of work and which category you fall into largely depends on your parents, according to new research from the University of Michigan.
Workers who are job-oriented are those just trying to make a living who much prefer the activities they pursue outside of the office. Career-oriented adults—your typical “workaholic”—value the social status and prestige that comes with professional achievement, and derive much of their identity from their jobs. Calling-oriented people do work that they are passionate about because they want to have a positive impact on the world.
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There’s no doubting that worldwide, kids are out of work. In the United States alone, the unemployment rate for 15 to 24-year-olds is about 16 percent, nearly twice the national average. In parts of Europe, the figures are much worse, with a whopping 56 percent youth unemployment rate in Spain alone — representing about 900,000 people.
But do these high numbers represent a global labor market crisis that imperils future growth, as the headlines warn? Maybe not. Maybe instead, they’re evidence of a generation of college graduates determined not to settle, which bodes well for our future.
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Just seven years ago, the Texas Legislature mandated that all high schoolers pass two algebra courses and geometry to graduate. This summer, the state reversed course, easing its strict math, science, and social-studies requirements to free up class time for job training.
Texas legislators want to create a more flexible system that helps students who aren’t headed to four-year colleges enter the workforce. And it’s not just Texas. State legislatures nationwide are enacting laws to promote career and technical education and workforce training in high school.
But that approach carries risks. While it’s true that not all students will go on to college, pulling back on college preparatory coursework has to be handled carefully in a state like Texas, with its hundreds of thousands of low-income and minority students. They’re the students who would benefit from college the most—and who need the most help getting there.
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All the drones, synthetic biologists, and self-driving cars notwithstanding, the story of how companies quantify, analyze, and try to predict your job performance may be the most important story in technology.
That is to say, when we look back in 20 years about what has changed in our lives, we will be able to find this thread of data-driven personnel decision making as the thing that’s changed people’s lives the most.
My colleague Don Peck has an unnerving feature in this month’s magazine on precisely this issue: “They’re Watching You At Work.” I highly encourage you to absorb this tale’s anecdotes and data.
After reading it, your gut may feel optimistic, like his, or queasy, like mine. Because the “Moneyballing” of human resources and corporate management has already begun, and who is going to stop it?
Peck’s reporting turned up some amazing/horrifying details about the current prevalence of data-driven corporate practices. For example, he writes, “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).”
Maybe that’s nice from a bottom-line perspective, but imagine working at Harrah’s: "Hey, Alexis, your smile ratio was down today. Keep those lip corners up, buddy!"
Do we want to live in that world?
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In 2003, thanks to Michael Lewis and his best seller Moneyball, the general manager of the Oakland A’s, Billy Beane, became a star. The previous year, Beane had turned his back on his scouts and had instead entrusted player-acquisition decisions to mathematical models developed by a young, Harvard-trained statistical wizard on his staff. What happened next has become baseball lore. The A’s, a small-market team with a paltry budget, ripped off the longest winning streak in American League history and rolled up 103 wins for the season. Only the mighty Yankees, who had spent three times as much on player salaries, won as many games. The team’s success, in turn, launched a revolution. In the years that followed, team after team began to use detailed predictive models to assess players’ potential and monetary value, and the early adopters, by and large, gained a measurable competitive edge over their more hidebound peers.
That’s the story as most of us know it. But it is incomplete. What would seem at first glance to be nothing but a memorable tale about baseball may turn out to be the opening chapter of a much larger story about jobs. Predictive statistical analysis, harnessed to big data, appears poised to alter the way millions of people are hired and assessed.
Yes, unavoidably, big data. As a piece of business jargon, and even more so as an invocation of coming disruption, the term has quickly grown tiresome. But there is no denying the vast increase in the range and depth of information that’s routinely captured about how we behave, and the new kinds of analysis that this enables. By one estimate, more than 98 percent of the world’s information is now stored digitally, and the volume of that data has quadrupled since 2007. Ordinary people at work and at home generate much of this data, by sending e-mails, browsing the Internet, using social media, working on crowd-sourced projects, and more—and in doing so they have unwittingly helped launch a grand new societal project. “We are in the midst of a great infrastructure project that in some ways rivals those of the past, from Roman aqueducts to the Enlightenment’s Encyclopédie,” write Viktor Mayer-Schönberger and Kenneth Cukier in their recent book, Big Data: A Revolution That Will Transform How We Live, Work, and Think. “The project is datafication. Like those other infrastructural advances, it will bring about fundamental changes to society.”
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And the two groups with the strongest preference for male bosses were women and Republicans. Yes, women.
In the early 1970s, a group of antiwar psychiatrists, most prominently Robert Jay Lifton, renowned for his work on the traumatic impact of Hiroshima, became concerned about the corrosive effect of the Vietnam War on the minds of the men who fought it. As Lifton told a Senate Committee in 1970, the veteran “returns as a tainted intruder…likely to seek continuing outlets for a pattern of violence to which they have become habituated.” To Lifton, the process of readjustment was one of “rehumanization.”
The stereotype of the mentally scarred vet that seized the public imagination during the Vietnam conflict lingers to this day, in part due to the media’s infatuation with the theme. Films such as Taxi Driver, Rambo, and Coming Home portrayed the veteran as a “walking time bomb.” Print media told much the same story. In 1972, the New York Times ran a front-page story, “Postwar Shock Is Found to Beset Veterans Returning from the War in Vietnam,” reporting that half of all Vietnam veterans were “psychiatric casualties of war” in need of “professional help to readjust.”
Today, according to a 2012 poll conducted by Greenberg Quinlan Rosner Research, over half of the public believes that the majority of post 9/11 veterans suffer from posttraumatic stress disorder. It’s a belief that could be hindering, rather than helping, servicemembers returning home from Afghanistan and Iraq.
Let’s begin with the fact that black unemployment is higher than Hispanic unemployment, which itself is higher than white unemployment. This hasn’t just been true for the last year, or the last decade. It’s been true for the last four decades and beyond.
And we’ll continue with the fact that, when you look at participation rates over the same 40 years, Hispanic men work more often than white men, who consistently work more than black men. Among women, the trend has been the mirror opposite and just as unchanging. Black women have consistently worked more often than white women, who have consistently worked more often than Hispanic women.
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