Rethinking
By S. Srinivasan. 2021
This chapter introduces the study’s orientation to understanding peacemaking in civil wars and different attempts to explain failed peacemaking in the Sudans. The chapter critiques mainstream peacemaking scholarship – from bargain approaches in realist and strategic studies to liberal democratic constitutionalism and reformist statebuilding – as well as prominent alternatives from political economy and conflict and peace studies, highlighting that they share in common problematic conceptions of politics as amenable to logics of ‘making’. This clears ground for the book’s novel theoretical critique of peacemaking drawing on Hannah Arendt’s political thought. Contemporary peacemaking risks undervaluing the political component in civil wars, risks emphasizing making an edifice for politics over civil political action itself, and risks producing means that violently overrun the sought after ends of peace. The chapter calls for a tragic understanding of peacemaking that compels, first of all, a need to carefully rethink what peacemaking is doing in attempts to make or build peace.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Deng-Ping Fan
- Ge-Peng Ji
- Ling Shao
- Ming-Ming Cheng
- Wenguan Wang
- Jianbing Shen
- Guolei Sun
- Jiemin Fang
Citations in the corpus
- Fast Neural Network Adaptation via Parameter Remapping and Architecture Search (2020, background): “Though some (He et al., 2018) recently demonstrates that pre-training is not always necessary for accuracy considerations, training from scratch on the target task still takes more iterations than fine-tuning from a pre-trained model.”
- Vision Transformer for Small-Size Datasets (2021, background): “PiT [12] produced various receptive fields through spatial dimension reduction based on the pooling structure of a convolutional layer.”
- Camouflaged Object Detection (2020, background): “Recently released datasets [10, 15, 16] have shown that establishing a taxonomic system is crucial when creating a large-scale dataset.”
- SAM-Adapter: Adapting Segment Anything in Underperformed Scenes (2023, background): “…to the remarkable advancements in deep-learning-based methods such as fully convolutional networks (FCN) [35], encoder-decoder structures [41, 50, 14, 1, 29], dilated convolutions [3, 4, 5, 6, 34], pyramid structures [60, 5, 52, 6, 57], attention modules [51, 16, 62], and transformers [54, 48,…”