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Bill Fulkerson

Millennials and Gen Z are spreading coronavirus-but not because of parties and bars - 0 views

www.nationalgeographic.com/...ch-parties-bars-inequality-cvd

millennials COVID-19 pandemic gen-z

shared by Bill Fulkerson on 17 Sep 20 - No Cached
  • Bill Fulkerson on 17 Sep 20
     
    Younger generations are blamed for the pandemic's spread, but also face the brunt of the transmission risk that comes with keeping the economy going. 6 MINUTE READ BY REBECCA RENNER PUBLISHED SEPTEMBER 17, 2020 WHEN PARAMEDICS RUSHED the pregnant Honduran woman into the emergency room, 28-year-old Chuan-Jay Jeffrey Chen stood ready to receive her. It was April, and the pandemic had already taken over his final year as an emergency medicine resident. Of all the coronavirus patients surging into Massachusetts General Hospital in Boston, this 32-year-old patient remains Chen's most memorable. The woman was so short of breath she could barely speak, so Chen would need to intubate her-a tricky procedure that requires precision as well as speed. Every moment without oxygen causes a patient's chances of survival to decline; pregnancy further complicates the scenario by making airways swollen, causing blood pressure to drop more quickly. As Chen set to work and talked her through the steps in Spanish, he also tried to calm his own nerves. "I knew I had very little margin for error," says Chen. The woman's husband had been barred from entering the building because of coronavirus restrictionsgen-z
Bill Fulkerson

Bumble bee disease, reproduction shaped by flowering strip plants - 0 views

phys.org/...-bee-disease-reproduction.html

reproduction disease bee consequences

shared by Bill Fulkerson on 11 May 20 - No Cached
  • Bill Fulkerson on 11 May 20
     
    Flowering strips-pollinator-friendly rows of plants that increase foraging habitat for bees-can help offset pollinator decline but may also bring risks of higher pathogen infection rates for pollinators foraging in those strips.
Steve Bosserman

Jane Jacobs's Theories on Urban Planning-and Democracy in America - The Atlantic - 0 views

www.theatlantic.com/...501104

democracy urban design ecosystems diversity neighborhoods

shared by Steve Bosserman on 17 Nov 16 - No Cached
  • Urban life was Jacobs’s great subject. But her great theme was the fragility of democracy—how difficult it is to maintain, how easily it can crumble. A city offered the perfect laboratory in which to study democracy’s intricate, interconnected gears and ballistics. “When we deal with cities,” she wrote in The Death and Life of Great American Cities (1961), “we are dealing with life at its most complex and intense.” When cities succeed, they represent the purest manifestation of democratic ideals: “Cities have the capability of providing something for everybody, only because, and only when, they are created by everybody.” When cities fail, they fail for the same reasons democracies fail: corruption, tyranny, homogenization, overspecialization, cultural drift and atrophy.
  • I was encouraged to believe that simple conformity results in stagnation for a society, and that American progress has been largely owing to the opportunity for experimentation, the leeway given initiative, and to a gusto and a freedom for chewing over odd ideas. I was taught that the American’s right to be a free individual, not at the mercy of the state, was hard-won and that its price was eternal vigilance, that I too would have to be vigilant.
  • Her 1,500-word speech, a version of which appears in Vital Little Plans, became the basis for The Death and Life of Great American Cities. Her main argument was Kirk’s: Small neighborhood stores, ignored by the planners in their grim demolition derby, were essential social hubs. She added that sidewalks, stoops, laundries, and mailbox areas were also indispensable centers of community activity, and that sterile, vacant outdoor space served nobody. “The least we can do,” she said, “is to respect—in the deepest sense—strips of chaos that have a weird wisdom of their own.”
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  • Reduced to a word, Jacobs’s argument is that a city, or neighborhood, or block, cannot succeed without diversity: diversity of residential and commercial use, racial and socioeconomic diversity, diversity of governing bodies (from local wards to state agencies), diverse modes of transportation, diversity of public and private institutional support, diversity of architectural style. Great numbers of people concentrated in relatively small areas should not be considered a health or safety hazard; they are the foundation of a healthy community. Dense, varied populations are “desirable,” Jacobs wrote,
  • Madison argued that as you increase the “variety of parties and interests” contained within a republic, “you make it less probable that a majority of the whole will have a common motive to invade the rights of other citizens.”
  • “We need all kinds of diversity,” Jacobs concluded in Death and Life, “so the people of cities can sustain (and further develop) their society and civilization.”
  • In her comparative study of fallen empires, Jacobs identifies common early indicators of decline: “cultural xenophobia,” “self-imposed isolation,” and “a shift from faith in logos, reason, with its future-oriented spirit … to mythos, meaning conservatism that looks backwards to fundamentalist beliefs for guidance and a worldview.” She warns of the profligate use of plausible denial in American politics, the idea that “a presentable image makes substance immaterial,” allowing political campaigns “to construct new reality.” She finds further evidence of our hardening cultural sclerosis in the rise of the prison-industrial complex, the prioritization of credentials over critical thinking in the educational system, low voter turnout, and the reluctance to develop renewable forms of energy in the face of global ecological collapse.
  • In the foreword to the 1992 Modern Library edition of Death and Life, Jacobs likens cities to natural ecosystems. “Both types of ecosystems,” she writes, “require much diversity to sustain themselves … and because of their complex interdependencies of components, both kinds of ecosystems are vulnerable and fragile, easily disrupted or destroyed.”
Bill Fulkerson

Financialization impedes climate change mitigation: Evidence from the early American so... - 0 views

advances.sciencemag.org/...e1601861.full

science climate change

shared by Bill Fulkerson on 03 Apr 17 - No Cached
  • Finance is an essential component of industrial change because it allows technologies to be developed before they can generate a return. But if finance no longer serves industrial change but instead prioritizes rent-seeking (seeking to increase its share of existing wealth without creating new sources of wealth), creative destruction of the present carbon-intensive industrial system cannot occur. The aim of this article is to investigate this issue through a study of the emergence of one low-carbon industry, solar photovoltaics (PV) in the United States. The focus is on the period after the first oil shock in 1973 until the end of the 1980s. The case is contrasted with the more successful development of the industry in Japan. In the late 1970s, American firms held 90% of the global market share; by 2005, it had declined to under 10%, whereas the Japanese share had risen to almost 50% (9). Changes to corporate governance and organization brought by financialization are identified as major causes of the difference in outcome.
  • One camp consisted of a small number of entrepreneurs who had been involved in producing solar cells for the space program or pioneered their application on Earth.
  • The other camp consisted of the energy policy bureaucracy and closely affiliated large manufacturing and energy corporations along with utilities (65).
Steve Bosserman

Why we find change so difficult, according to neuroscience - 0 views

www.nbcnews.com/...ording-neuroscience-ncna934011

adaptation change neuroscience

shared by Steve Bosserman on 14 Nov 18 - No Cached
  • “Emotionally and cognitively and executively the brain has established a lot of pathways,” says Dr. Sanam Hafeez, a licensed clinical psychologist and neuropsychologist. “The more you do something the more ingrained it becomes in neural pathways, much like how a computer that stores the sites you visit — when you log onto your browser, they will pop up because you use them a lot. Change is an upheaval of many things and the brain has to work to fit it into an existing framework.”
  • “You absolutely can and should teach your brain to change,” says Hafeez, noting that keeping the brain agile has been shown to help delay aging. “I've done quite a bit of work on the aging process and slowing that down. It starts with changing the aversion to change.”
  • “Let’s say you’re a financial planer who takes up knitting,” says Hafeez. “That is doing something very different, where the brain truly has to adapt new neural pathways. Learning a new skill like this have been shown to ward off dementia, aging and cognitive decline because it regenerates cellular activity. Learn a new language in middle age. You tax your brain by shaking things up and it’s effective for your body in the way HIIT is for your body.”
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  • “Most people won't try something new because they’re deathly afraid of failing,” notes Hafeez. “When you see that something is doable it makes you more receptive and brave. There's that emotional, therapeutic factor that is separate from the neural pathway factor. Over the years, we learn to succeed by viewing our previous failures and successes in a certain light and as we get older we lose sight of that. When you try a new thing it makes you more confident to try to do more new things.”
Steve Bosserman

Are You Creditworthy? The Algorithm Will Decide. - 0 views

undark.org/...redit-scoring-machine-learning

algorithms discrimination bias credit scoring

shared by Steve Bosserman on 12 May 18 - No Cached
  • The decisions made by algorithmic credit scoring applications are not only said to be more accurate in predicting risk than traditional scoring methods; its champions argue they are also fairer because the algorithm is unswayed by the racial, gender, and socioeconomic biases that have skewed access to credit in the past.
  • Algorithmic credit scores might seem futuristic, but these practices do have roots in credit scoring practices of yore. Early credit agencies, for example, hired human reporters to dig into their customers’ credit histories. The reports were largely compiled from local gossip and colored by the speculations of the predominantly white, male middle class reporters. Remarks about race and class, asides about housekeeping, and speculations about sexual orientation all abounded.
  • By 1935, whole neighborhoods in the U.S. were classified according to their credit characteristics. A map from that year of Greater Atlanta comes color-coded in shades of blue (desirable), yellow (definitely declining) and red (hazardous). The legend recalls a time when an individual’s chances of receiving a mortgage were shaped by their geographic status.
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  • These systems are fast becoming the norm. The Chinese Government is now close to launching its own algorithmic “Social Credit System” for its 1.4 billion citizens, a metric that uses online data to rate trustworthiness. As these systems become pervasive, and scores come to stand for individual worth, determining access to finance, services, and basic freedoms, the stakes of one bad decision are that much higher. This is to say nothing of the legitimacy of using such algorithmic proxies in the first place. While it might seem obvious to call for greater transparency in these systems, with machine learning and massive datasets it’s extremely difficult to locate bias. Even if we could peer inside the black box, we probably wouldn’t find a clause in the code instructing the system to discriminate against the poor, or people of color, or even people who play too many video games. More important than understanding how these scores get calculated is giving users meaningful opportunities to dispute and contest adverse decisions that are made about them by the algorithm.
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