标题： 基于机器学习和统计建模的孟加拉国七月革命镇压与动员混合方法分析 显示英文标题

标题： A Mixed-Methods Analysis of Repression and Mobilization in Bangladesh's July Revolution Using Machine Learning and Statistical Modeling

摘要： 2024年7月的孟加拉国革命是研究公民抵抗运动的一个里程碑事件。 本研究探讨了这一学生领导的平民起义成功的核心悖论：旨在镇压异议的国家暴力最终如何促成了运动的胜利。 我们采用混合方法的方法。 首先，我们开发了一个关于冲突时间线的定性叙述，以生成具体且可检验的假设。 然后，使用分解的、事件级别的数据集，我们采用多方法定量分析来剖析镇压与动员之间的复杂关系。 我们提供了一个框架来分析像7月革命这样的爆炸性现代起义。 初步的合并回归模型突出了抗议势头在维持运动中的关键作用。 为了隔离因果效应，我们指定了一个双向固定效应面板模型，该模型提供了直接且统计显著的局部镇压反效果的有力证据。 我们的向量自回归（VAR）分析提供了对致命暴力增加作出立即全国性动员的清晰视觉证据。 我们进一步证明了这种效果是非线性的。 结构断点分析显示，在冲突的早期阶段，反效果在统计上不显著，但被第一波致命暴力的催化道德冲击所触发，其画面在7月16日左右传播。 一种补充的机器学习分析（XGBoost，样本外R$^{2}$=0.65）从预测角度证实了这一点，识别出“对示威者使用过度武力”是全国升级的单一最主导预测因素。 我们得出结论，7月革命是由特定催化道德冲击引发的偶然性、非线性反效果驱动的，并因国家暴行的视觉奇观的病毒式反应而加速。 显示英文摘要

摘要： The 2024 July Revolution in Bangladesh represents a landmark event in the study of civil resistance. This study investigates the central paradox of the success of this student-led civilian uprising: how state violence, intended to quell dissent, ultimately fueled the movement's victory. We employ a mixed-methods approach. First, we develop a qualitative narrative of the conflict's timeline to generate specific, testable hypotheses. Then, using a disaggregated, event-level dataset, we employ a multi-method quantitative analysis to dissect the complex relationship between repression and mobilisation. We provide a framework to analyse explosive modern uprisings like the July Revolution. Initial pooled regression models highlight the crucial role of protest momentum in sustaining the movement. To isolate causal effects, we specify a Two-Way Fixed Effects panel model, which provides robust evidence for a direct and statistically significant local suppression backfire effect. Our Vector Autoregression (VAR) analysis provides clear visual evidence of an immediate, nationwide mobilisation in response to increased lethal violence. We further demonstrate that this effect was non-linear. A structural break analysis reveals that the backfire dynamic was statistically insignificant in the conflict's early phase but was triggered by the catalytic moral shock of the first wave of lethal violence, and its visuals circulated around July 16th. A complementary machine learning analysis (XGBoost, out-of-sample R$^{2}$=0.65) corroborates this from a predictive standpoint, identifying "excessive force against protesters" as the single most dominant predictor of nationwide escalation. We conclude that the July Revolution was driven by a contingent, non-linear backfire, triggered by specific catalytic moral shocks and accelerated by the viral reaction to the visual spectacle of state brutality.