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Here’s what I propose. In the 21st century, we flip Bloom’s taxonomy. Rather than starting with knowledge, we start with creating, and eventually discern the knowledge that we need from it.
"What constructing ought to denote, but perhaps never will (hence Levi and Latour's calls for a new term), is that the knowledge we produce is another object in the world, made from other objects in the world (including us). As one object among many, the knowledge we produce does not capture/represent in some pure way other objects in the world. It isn't "true" in that sense. As academics we already accept this across the campus. However it also isn't "untrue" or operating in a separate, noncommunicating realm from other objects. It isn't purely discursive or purely social. "
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But I can't believe that. I can't afford to believe that. If we believe that as humanists we cannot produce knowledge of real value with the strength to make changes in the world, then what would we be doing as teachers or scholars? We would be engaged in some kind of self-pleasuring activity, perhaps with the idea that our performances might instill in others (through some quasi-magical, sympathetic incantation) a similar practice of finding self-pleasure (or aesthetic appreciation) through a purely subjective/cultural/discursive encounter with the objects we study. No doubt there is a strong strand of such thinking in the humanities, especially in English, that goes back at least to Matthew Arnold (though in his case the self-pleasure was imbuded with a chaste religiosity rather than the psycho-sexual implications one probably sees here). However, no one would imagine self-pleasure as the sole goal of humanistic study. We must be able to produce knowledge that has the strength to make changes. And that requires an understanding of how knowledge is constructed and operates in a world that isn't divided into natural, social, and discursive realms. And this is as true for our research and teaching as it is for assessment.
"The intellectual culture of scientism clouds our understanding of science itself. What’s more, it eclipses alternative ways of knowing — chiefly the philosophical — that can actually yield greater certainty than the scientific. While science and philosophy do at times overlap, they are fundamentally different approaches to understanding. So philosophers should not add to the conceptual confusion that subsumes all knowledge into science. "
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In sum, philosophy is not science. For it employs the rational tools of logical analysis and conceptual clarification in lieu of empirical measurement. And this approach, when carefully carried out, can yield knowledge at times more reliable and enduring than science, strictly speaking. For scientific measurement is in principle always subject to at least some degree of readjustment based on future observation. Yet sound philosophical argument achieves a measure of immortality.
So if we philosophers want to restore philosophy’s authority in the wider culture, we should not change its name but engage more often with issues of contemporary concern — not so much as scientists but as guardians of reason. This might encourage the wider population to think more critically, that is, to become more philosophical.
"David closes by returning to his original question: why were old knowledge systems so fragile? These systems assumed knowledge was bounded, settled, orderly and proceeded step by step. But that’s not what knowledge feels like in the age of the internet. It feels unbounded, overwhelming, unsettled, messy, linked and governed by our interests. And those properties are the properties of what it means to be human in the world.
“Networked knowledge may or may not be truer about the world, but is is truer about knowing… This crazy approach to knowledge feels familiar to us, because it’s how we tend to know.” He closes with an observation that’s both hopeful and unsettling: “What we have in common is a shared world about which we disagree, not a common knowledge we share and can collectively come to.”"
The division of cognitive labor is fundamental to all cultures. Adults have a strong sense of how knowledge is clustered in the world around them and use that sense to access additional information, defer to relevant experts, and ground their own incomplete understandings. One prominent way of clustering knowledge is by disciplines similar to those that comprise the natural and social sciences. Seven studies explored an emerging sense of these discipline-based ways of clustering of knowledge. Even 5-year-olds could cluster knowledge in a manner roughly corresponding to the departments of natural and social sciences in a university, doing so without any explicit awareness of those academic disciplines. But this awareness is fragile early on and competes with other ways of clustering knowledge. Over the next few years, children come to see discipline-based clusters as having a privileged status, one that may be linked to increasingly sophisticated assumptions about essences for natural kinds. Possible mechanisms for this developmental shift are examined.
Concepts seem to consist of both an associative component based on tabulations of feature typicality and similarity judgments and an explanatory component based on rules and causal principles. However, there is much controversy about how each component functions in concept acquisition and use. Here we consider two assumptions, or dogmas, that embody this controversy and underlie much of the current cognitive science research on concepts. Dogma 1: Novel information is first processed via similarity judgments and only later is influenced by explanatory components. Dogma 2: Children initially have only a similarity-based component for learning concepts; the explanatory component develops on the foundation of this earlier component. We present both empirical and theoretical arguments that these dogmas are unfounded, particularly with respect to real world concepts; we contend that the dogmasarise from a particular species of empiricism that inhibits progress in the study of conceptual structure; and finally, we advocate the retention of a hybrid model of the structure of knowledge despite our rejection of these dogmas.
"After a hard look, I realized that they had bombed on the questions that challenged their position. A full tabulation of all 17 questions showed that no group clearly out-stupids the others. They appear about equally stupid when faced with proper challenges to their position. "
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Shouldn’t a college professor have known better? Perhaps. But adjusting for bias and groupthink is not so easy, as indicated by one of the major conclusions developed by Buturovic and sustained in our joint papers. Education had very little impact on responses, we found; survey respondents who’d gone to college did only slightly less badly than those who hadn’t. Among members of less-educated groups, brighter people tend to respond more frequently to online surveys, so it’s likely that our sample of non-college-educated respondents is more enlightened than the larger group they represent. Still, the fact that a college education showed almost no effect—at least for those inclined to take such a survey—strongly suggests that the classroom is no great corrective for myside bias. At least when it comes to public-policy issues, the corrective value of professional academic experience might be doubted as well.
Discourse affords some opportunity to challenge the judgments of others and to revise our own. Yet inevitably, somewhere in the process, we place what faith we have.
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Robert K. Merton’s “functionalist” sociology viewed “science” as a kind of Weberian ideal type — a form of thought that is identifiable by its peculiar, philosophically-defined characteristics. Merton’s sociology of science held that this thought could also be identified with social behaviors, characterized by a set of “norms”, which made the thought possible.
The Merton Thesis (which slightly predates Merton’s enumeration of science’s norms) holds that the rise of science in early-modern England could be linked to the social behaviors valued by the Puritanism of that milieu. This was the subject of Merton’s PhD thesis and his 1938 book Science, Technology and Society in Seventeenth-Century England.
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Shapin observed that the link Merton drew between Puritanism and seventeenth-century English science was a matter of happenstance rather than determinism. According to Merton, science requires certain “values” and “sentiments” allowing intellectual individualism, and fostering not only an interest in the transcendent, but also secular improvement. It so happened that these values and sentiments were to be found in Puritan asceticism and sense of social obligation, which thus provided a social context in which science could develop.
Definitively, this was not to say that Puritanism provided a unique source of these values and sentiments, or that science did not have other roots. It was obviously possible for science to develop in Catholic contexts as well, despite the less hospitable value system of Catholicism. The confluence of values simply seemed to promise some insight into the growth of science in a particular time and place.
Very interesting summary of debates on SSK and Mertonian science studies during the mid-20c. Describes the move away from functional, ideal-type, descriptions a la Merton to more historically specific microhistories a la Daston.
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However, at the same time, another critique questioned the basic validity of that framework. This critique shared the SSK critique’s interest in describing actual scientific work, but, like Mertonian sociology, it focused on scientists’ and others’ sense of the essence of scientific culture without directly addressing knowledge-production processes. This critique held that, because “functionalist” ideal-type systems of scientific behavior could not actually be found in their pure form, such systems did not meaningfully exist. Legitimate sociology had to be obtained inductively from the empirical record, as studied by historians and ethnologists.
"The divisions between neurology and psychiatry suggested in the image above stir up lots of interesting questions not only about what we consider to be “neurological” or “psychiatric”, but more generally about the social production of knowledge."
"But information networks matter more than the devices we use to access them, or the applications that run on those devices. The key to the automation of knowledge work that Schrage righly prescribes isn’t learning how to use smartphones or tablets. Rather, it’s learning and then applying core principles that govern information networks. "
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Not even a decade ago, organizations had systems analysts design and impose norms demanding total employee compliance. Technology was as much an enforcement tool as a process platform. A decade hence, dynamic optimization will be an ongoing negotiation between the local expertise of the digitally-deviced worker and the analytic prowess of the centralizing system. Of course there will be top-down diktats but a growing number of them will come from the tricks, shortcuts, and hacks workers use to make themselves more efficient.
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Atul Gawande's best-selling Checklist Manifesto beautifully articulated how even the most brilliant and conscientious knowledge workers should rely on checklists to make sure they're doing the right thing at the right time in the right way. These checklists are ideal ingredients for the algorithms and apps that make effective auto-automation possible. As a former student of "artificial intelligence" software, I'd observe that formal and informal checklists enable simple but powerful "expert systems" to emerge. When expert system checklists get married to the social media networking, photographic, and GPS "intelligence" of digital devices, the opportunities for auto-automation explode.
"This article argues for the following: 1. Information is a thing to be handled and controlled; knowledge is not. 2. Knowledge can be managed only indirectly, through the management of information. 3. Personal knowledge management (PKM) is, therefore, best regarded as a subset of personal information management (PIM) — but a very useful subset addressing important issues that otherwise might be overlooked."
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Both of these stories captures something we all understand on a deep intuitive level, but our creative egos sort of don’t really want to accept: And that is the idea that creativity is combinatorial, that nothing is entirely original, that everything builds on what came before, and that we create by taking existing pieces of inspiration, knowledge, skill and insight that we gather over the course of our lives and recombining them into incredible new creations.
"My aim in this paper is to give a philosophical analysis of how, precisely, technology can be a condition for gaining scientific knowledge. My concern is with what scientists can know in practice, given their particular contingent conditions, including available technology, rather than what can be known “in principle” by a hypothetical entity like Laplace’s Demon. I begin with the observation that what we know depends on what we can do. For example, in science, gaining certain knowledge depends of having certain evidence. This makes the ability to gather that evidence a necessary condition for gaining the knowledge. "
But - here’s what drives me crazy as a reporter - did you say anything about it before? It doesn’t seem a coincidence that voter apathy, financial illiteracy, and government spending have all risen in tandem. As reporters, we’re trying to inform you so that you can be a fully functioning citizen. We tell you: here’s the debate. Here’s what people are saying on both sides. And too often, the response we get back is, “how DARE you tell me what those people think? La la la la, I can’t hear you!”
"Here’s a theory. It’s to do with organizational brittleness. Here are some background principles:
- The death rate for firms generally is high. Of the UK’s biggest employers in 1907, only three are still independent, stock market-listed companies today.
- Companies embody specific vintages of organizational capital. Their expertise depends upon the state of technology when they were formed. It’s rare for a firm to transform itself from one activity to a completely different one; Nokia, which used to be a cable firm, is a rare exception - and a less healthy one than it seemed a few years ago.
- Because organizational capital is fixed, firms have “very limited capacities to acquire knowledge.” Rather than adapting to new conditions, firms die."
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