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Titlebook: Making the Moral Case for Social Sciences; Stemming the Tide Karl Spracklen Book 2016 Palgrave Macmillan, a division of Macmillan Publisher

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發(fā)表于 2025-3-21 19:34:54 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Making the Moral Case for Social Sciences
副標題Stemming the Tide
編輯Karl Spracklen
視頻videohttp://file.papertrans.cn/622/621798/621798.mp4
圖書封面Titlebook: Making the Moral Case for Social Sciences; Stemming the Tide Karl Spracklen Book 2016 Palgrave Macmillan, a division of Macmillan Publisher
描述.The social sciences have a legitimacy problem in the modern world. The natural sciences are viewed as ‘proper science‘ by journalists and policy-makers because they discover ‘truths‘, make money, and help governments solve problems. In turn, defenders of the social sciences borrow the language of instrumentality, profit and policy impact. Karl Spracklen, by contrast, makes the moral case for the social sciences, arguing that they are a necessary social good capable of fighting inequality and revealing the workings of hegemonic power.. .
出版日期Book 2016
關(guān)鍵詞Social sciences; power; hegemony; moral case; social good; Defence; government; Moral; morality; quality; soci
版次1
doihttps://doi.org/10.1057/9781137577917
isbn_ebook978-1-137-57791-7
copyrightPalgrave Macmillan, a division of Macmillan Publishers Limited 2016
The information of publication is updating

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The Moral Case for the Social Sciences,shing; and, most importantly, they are good because they show humans how to resist inequality. Spracklen makes the case that the ability to see the world through a critical lens is the necessary moral and social good that this critical sociology gives to the social sciences, and through the social s
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ven directed network can be formulated as follows: partition vertices into levels such that, ideally, there are only forward edges, that is, edges from upper levels to lower levels. In practice, the ideal case is impossible, so instead we minimize some penalty function on the backward edges. One pra
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Karl Spracklenmodels without access to past data. Current methods focus only on selecting a sub-network for a new task that reduces forgetting of past tasks. However, this selection could limit the forward transfer of . past knowledge that helps in future learning. Our study reveals that satisfying both objective
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