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Titlebook: Making ‘Postmodern‘ Mothers; Pregnant Embodiment, Meredith Nash Book 2012 Palgrave Macmillan, a division of Macmillan Publishers Limited 20

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發(fā)表于 2025-3-21 18:53:52 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Making ‘Postmodern‘ Mothers
副標(biāo)題Pregnant Embodiment,
編輯Meredith Nash
視頻videohttp://file.papertrans.cn/622/621804/621804.mp4
叢書名稱Genders and Sexualities in the Social Sciences
圖書封面Titlebook: Making ‘Postmodern‘ Mothers; Pregnant Embodiment, Meredith Nash Book 2012 Palgrave Macmillan, a division of Macmillan Publishers Limited 20
描述Based on interviews with pregnant women, this book provides a multi-disciplinary empirical account of pregnant embodiment and how it relates to wider sociological and feminist discourses about gender, bodies, ‘fitness‘, ‘fat‘, celebrity and motherhood.
出版日期Book 2012
關(guān)鍵詞feminism; gender; Image; sociology; women
版次1
doihttps://doi.org/10.1057/9781137292155
isbn_softcover978-1-349-34713-1
isbn_ebook978-1-137-29215-5Series ISSN 2947-8782 Series E-ISSN 2947-8790
issn_series 2947-8782
copyrightPalgrave Macmillan, a division of Macmillan Publishers Limited 2012
The information of publication is updating

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發(fā)表于 2025-3-21 20:17:36 | 只看該作者
板凳
發(fā)表于 2025-3-22 03:03:32 | 只看該作者
Meredith Nash scarce. While many of these approaches rely on a cross-entropy loss function (CE), recent advances have shown that the supervised contrastive loss function (SupCon) can be more effective. Additionally, unsupervised contrastive learning approaches have also been shown to capture high quality data re
地板
發(fā)表于 2025-3-22 04:41:15 | 只看該作者
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發(fā)表于 2025-3-22 08:56:56 | 只看該作者
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發(fā)表于 2025-3-22 16:50:05 | 只看該作者
Meredith Nashng methods for learning user representations are usually based on supervised frameworks such as demographic prediction and product recommendation. In addition, these methods highly rely on labeled data to learn user-representation models, and the user representations learned using these methods can
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發(fā)表于 2025-3-22 19:05:12 | 只看該作者
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發(fā)表于 2025-3-23 00:16:51 | 只看該作者
Meredith Nashcommerce to computational biology. However, generating satisfactory video embeddings and putting them into practical use to improve the performance of recommendation tasks remains a challenge. In this paper, we present a video embedding approach named Equuleus, which learns video embeddings from use
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發(fā)表于 2025-3-23 04:59:06 | 只看該作者
Meredith Nashnd reliable prediction crucial for mitigating potential impacts. This paper contributes to the growing body of research on deep learning methods for solar flare prediction, primarily focusing on highly overlooked near-limb flares and utilizing the attribution methods to provide a post hoc qualitativ
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發(fā)表于 2025-3-23 08:54:59 | 只看該作者
Meredith Nashin analyzing complex control flows. Current machine learning systems, mostly based on artificial neural networks, have the inherent problem that models cannot be verified since the decisions depend on probabilities. To bridge the gap between handcrafted rule systems and probability-based systems, ou
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