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Bill Fulkerson

Is Peer Review a Good Idea? | The British Journal for the Philosophy of Science | Oxfor... - 0 views

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    Prepublication peer review should be abolished. We consider the effects that such a change will have on the social structure of science, paying particular attention to the changed incentive structure and the likely effects on the behaviour of individual scientists. We evaluate these changes from the perspective of epistemic consequentialism. We find that where the effects of abolishing prepublication peer review can be evaluated with a reasonable level of confidence based on presently available evidence, they are either positive or neutral. We conclude that on present evidence abolishing peer review weakly dominates the status quo.
Steve Bosserman

Why the utopian vision of William Morris is now within reach - Vasilis Kostakis and Wol... - 0 views

  • In News from Nowhere, Morris imagined a world in which human happiness and economic activity coincided. He reminds us that there needs to be a point to labour beyond making ends meet – and there is. Unalienated labour creates happiness for all – consumer and creator; whereas modern capitalism, in contrast, has created a treadmill in which this aspect of work has been lost. Capitalism, he explains, locks the capitalist into a horrible life, which leads nowhere but the grave.
  • No matter where they are based, people today can use the internet to cooperate and globally share the products of their cooperation as a commons. Commons-based peer production (usually abbreviated as CBPP) is fundamentally different from the dominant modes of production under industrial capitalism. In the latter, owners of means of production hire workers, direct the work process, and sell products for profit-maximisation. Think how typical multinational corporations are working. Such production is organised by allocating resources through the market (pricing) and through hierarchical command. In contrast, CBPP is in principle open to anyone with the relevant skills to contribute to a common project: the knowledge of every participant is pooled.
  • These participants might be paid, but not necessarily. Since commons-based projects are open systems, anyone with the right knowledge and skills can contribute, either paid by companies, clients or not at all.
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  • What is light (knowledge, design) becomes global; what is heavy (manufacturing) is local and shared
  • When social groups appropriate a particular technology for their own purposes, then social, political and economic systems can change
  • CBPP allows contributions based on all kinds of motivations such as the need to learn or to communicate. However, most importantly, a key incentive is the desire to create something mutually useful to those contributing. This also generally means that people contribute because they find it meaningful and useful, and they believe the resulting product worthwhile.
  • The design is developed and improved as a global digital commons, while the manufacturing often takes place through shared infrastructures and with local biophysical conditions in mind.
Bill Fulkerson

They Made a Movie Out of It | James Pogue - 0 views

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    At least some of my peers are now handing over their working lives to producing cynical content rigged to fit the desires of streaming services, which, when you think about it, is a small tragedy for a world as fucked as ours. Most of the good writers are not. But how could you not at least think about these imperatives when a strange new amalgam of Hollywood and tech offers the greatest rewards for a hit second novel and when magazines pay below rates that were standard three decades ago? Almost all notable book-length nonfiction written in this country emerges as an expansion of work that was first published by a magazine, so-whether they admit it or not-magazines are the incubators for the nonfiction writers who describe our world. But these outlets generally make not the barest pretense of trying to pay writers enough to build a life. Instead, editors at prestige outlets increasingly view writing as germinal IP.
Bill Fulkerson

It's (still) not about trust: No one should buy AI if governments won't enforce liability - 0 views

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    Yet another government agency has asked me how to get people to trust AI. No one should trust AI. AI is not a peer that we need to allow to make mistakes sometimes but tolerate anyway. AI is a corporate product. If corporations make understandable mistakes they can be let off the hook, but if they whether with negligence or intent produce faulty products that do harm they must be held to account.
Steve Bosserman

Every future we think of follows one of four narratives - 0 views

  • Journalists can’t see the future, but they are able to peer through the lens of history to better understand the present. It’s a founding principle of Retro Report, the co-producer of this series. The future may be starkly different than the present, but it’ll be easier to understand once you uncover its deep continuity with the past. The social and technical transformations we’re currently living through are profound, but this isn’t the first time rapid, singular change has occurred. Before computer networks disrupted our communications, networks of steel rails and grids of artificial light upended our very concepts of space and time, day and night. Subtract trains and light bulbs from a modern city, and how much of it is even left?
  • The future has a history. And the stories we tell about incoming change—the stories we’ve always told about such changes—fall into consistent patterns. Dator gained some of his stature in future studies with his famous observation that predictions about the future—whether they’re coming from a corporate spreadsheet, a church pulpit or Hollywood—all boil down to roughly four scenarios. Growth that keeps going. Transformation upending the past. Collapse of the present order. And  discipline imposed, in some cases, to hold such collapse at bay.
  • “Most people, through their education, and through their acculturation, are locked into a single view of the future. They have never been encouraged to think about these alternatives, or forced to think about them,” Dator says.
Steve Bosserman

Why America is the World's Most Uniquely Cruel Society - 0 views

  • Any theory of being American must explain one salient and striking fact: cruelty. America is the most cruel nation among its peers — even among most poor countries today. It is something like a new Rome. It has little, if any, functioning healthcare, education, transport, media, no safety nets, no stability, security. The middle class is collapsing, and life expectancy is falling. Young people die for a lack of insulin they cannot crowdfund. Elderly middle-class people live and die in their cars. Kids massacre each other in schools — when they’re not self-medicating the pain of it all away. The combination of these pathologies happens nowhere else — not a single place — in the world. Not even Pakistan, Costa Rica, or Rwanda. Hence, the world is aghast daily at the depths of American cruelty — yet somehow, they seem bottomless.(Of course I don’t mean that all Americans are cruel. I just mean that in the same way we say countries have attitude, dispositions, that there’s such a thing as a French or German national attitude or disposition, so, too there is an American one. Nor do I mean America is “the most cruel society in the world”. Can we really ever judge that? But it is uniquely cruel — a kind of special example — in weird, needless, and singular ways.)Let me throw that into relief. Scandinavians are the happiest, longest-lived, and most prosperous people in the world because they do not punish one another constantly — but lift one another up. But Americans do not believe this reality. The underlying sentiment that unites America’s manifold problems is a myth of cruelty.
Steve Bosserman

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

  • 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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