论文标题
印度nudges包含共同19-19大流行：使用基于机器学习的主题建模的反应性公共政策分析
India nudges to contain COVID-19 pandemic: a reactive public policy analysis using machine-learning based topic modelling
论文作者
论文摘要
印度于2020年3月25日在1920年3月25日锁定了13亿人。它的经济成本估计为980亿美元，而社会成本仍然未知。这项研究调查了政府如何制定反应性政策，以抵抗其政策部门的冠状病毒。主要数据是根据政府计划，政策，计划计划和成就的新闻稿的新闻信息局（PIB）收集的。由PIB的396个文档创建了260,852个单词的文本语料库。在文本语料库上执行了使用潜在Dirichlet分配（LDA）算法的无监督的基于机器的主题建模。这样做是为了提取政策部门中的高概率主题。提取主题的解释是通过推动理论镜头来得出政府关键政策启发式方法的。结果表明，大多数干预措施都是通过使用外部触发器来产生内源性小动物的。值得注意的是，印度总理的轻推对于在全国范围内对锁定和社会疏远规范产生畜群影响至关重要。 A similar effect was also observed around the public health (e.g., masks in public spaces; Yoga and Ayurveda for immunity), transport (e.g., old trains converted to isolation wards), micro, small and medium enterprises (e.g., rapid production of PPE and masks), science and technology sector (e.g., diagnostic kits, robots and nano-technology), home affairs (e.g.,监视和锁定），城市（例如无人机，GIS-Tools）和教育（例如在线学习）。得出结论，要利用这些启发式方法对于锁定地役权计划至关重要。
India locked down 1.3 billion people on March 25, 2020 in the wake of COVID-19 pandemic. The economic cost of it was estimated at USD 98 billion, while the social costs are still unknown. This study investigated how government formed reactive policies to fight coronavirus across its policy sectors. Primary data was collected from the Press Information Bureau (PIB) in the form press releases of government plans, policies, programme initiatives and achievements. A text corpus of 260,852 words was created from 396 documents from the PIB. An unsupervised machine-based topic modelling using Latent Dirichlet Allocation (LDA) algorithm was performed on the text corpus. It was done to extract high probability topics in the policy sectors. The interpretation of the extracted topics was made through a nudge theoretic lens to derive the critical policy heuristics of the government. Results showed that most interventions were targeted to generate endogenous nudge by using external triggers. Notably, the nudges from the Prime Minister of India was critical in creating herd effect on lockdown and social distancing norms across the nation. A similar effect was also observed around the public health (e.g., masks in public spaces; Yoga and Ayurveda for immunity), transport (e.g., old trains converted to isolation wards), micro, small and medium enterprises (e.g., rapid production of PPE and masks), science and technology sector (e.g., diagnostic kits, robots and nano-technology), home affairs (e.g., surveillance and lockdown), urban (e.g. drones, GIS-tools) and education (e.g., online learning). A conclusion was drawn on leveraging these heuristics are crucial for lockdown easement planning.
