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Titlebook: Romantic Women Writers and Arthurian Legend; The Quest for Knowle Katie Garner Book 2017 The Editor(s) (if applicable) and The Author(s) 20

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樓主: 戲弄
31#
發(fā)表于 2025-3-26 21:30:10 | 只看該作者
how they shaped their imaginative responses.Connects VictoriThis book?reveals the breadth and depth of women’s engagements with Arthurian romance in the late eighteenth and early nineteenth centuries. Tracing the variety of women’s responses to the medieval revival through Gothic literature, travel
32#
發(fā)表于 2025-3-27 04:39:56 | 只看該作者
,Arthuriana for the ‘Fair Sex’: Gender Politics and the Reception of Romance,ian material was considered to be outside the boundaries of acceptable female knowledge. Particular attention is given to a selection of Thomas Percy’s Arthurian ballads produced for young women, while the closing discussion examines how contemporary gender debates influenced the marketing of Romantic editions of Malory’s .
33#
發(fā)表于 2025-3-27 06:55:41 | 只看該作者
,Next Steps: Recovering the Arthurian Past in Women’s Travel and Topographical Writing,, Eleanor Anne Porden, Anna Sawyer, and Mary Russell Mitford is examined. As part of its attention to places and spaces, Garner also addresses the nationalist impulse at stake in treatments of the Arthurian myth and concludes that English women poets ultimately failed to appropriate the legend successfully in their verse.
34#
發(fā)表于 2025-3-27 10:29:12 | 只看該作者
35#
發(fā)表于 2025-3-27 14:59:33 | 只看該作者
Book 2017g the Arthurian interests of the best-selling female poets of the day, Felicia Hemans and Letitia Elizabeth Landon, and uncovering those of many of their contemporaries, the Arthurian myth in the Romantic period is a vibrant location for debates about the function of romance, the role of the imagination, and women’s place in literary history.?.
36#
發(fā)表于 2025-3-27 20:23:58 | 只看該作者
37#
發(fā)表于 2025-3-27 23:14:25 | 只看該作者
38#
發(fā)表于 2025-3-28 02:33:43 | 只看該作者
Ludwig Hempele consider both homogeneous and heterogeneous client data scenarios, including scenarios where training on the aggregated data is suboptimal due to biases in the data. In addition to deep learning methods, we cover unsupervised settings such as mixture models, topic models, and hidden Markov models.
39#
發(fā)表于 2025-3-28 08:02:20 | 只看該作者
40#
發(fā)表于 2025-3-28 13:36:14 | 只看該作者
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