Computational Statistics to Tackle Modern Slavery

解决现代奴隶制问题的计算统计

• 批准号: MR/X034992/1
• 负责人: Rowland Seymour
• 金额: $ 147.27万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX034992%2F1
• 关键词: Computational; Statistics; Tackle; Modern; Slavery

If we are to meet the United Nation's Sustainable Development Goals by their target of 2030, we need to develop better statistical methods to map the prevalence of vulnerable populations. In this fellowship, I will A. carry out foundational research into effective computational statistics methods for hidden populations, B. use the methods to map modern slavery at local, national and international levels, and C. work with my project partners to change policy based on our evidence-based research. To meet the Sustainable Development Goals, we need to measure how close we are to meeting them, quantify who is most in need of support and evaluate how successful interventions are in creating sustainable development. Take, for example, victims of modern slavery. Victims are often marginalised and hidden, with abuses going unreported and unmonitored. Estimating how many victims there are, where the abuses are happening and evaluating the effectiveness of interventions to support victims remain a challenge to the field of modern slavery and sustainable development more broadly. Data about victims and abuses is often noisy, poor quality or simply not collected. Developments in computational statistics can be really powerful here. They will provide a framework to deal with poor quality and missing data, while simultaneously avoiding specific and arbitrary assumptions about how the abuses are happening. Current methods require researchers to make specific assumptions about the abuses they are modelling which are difficult to justify from the data. The methods I develop will move away from this, instead making more general, mathematical assumptions. This will allow the data to speak for itself and can provide better counterfactual evidence and more realistic conclusions. To meet this aim, I bring a strong track record of developing these methods for epidemics, where my methods have been shown to reduce the need for specific assumptions when the data is poor quality. However, this flexibility comes at the cost of a larger computational burden, increased uncertainty in the results, and a requirement for technical expertise when using the methods. To speed up progress to meeting the Sustainable Development Goals, researchers need methods that can be used in practice. I will lead the development of effective computational statistical methods. By reducing the computational burden, providing mechanisms to deal with the uncertainty in the results, and making methods easy to implement, they will become much more attractive to non-statisticians. I have already shown how my developments can considerably reduce the data collection burden when mapping poverty, making these methods more attractive to research and organisations working in poverty reduction. A key part of this fellowship is collaboration with a research software engineer who can develop data systems and software that other researchers and organisations can use to implement my methods. I will use my methods to solve pressing problems in modern slavery and advance the field to meet the UN's goal to end slavery by 2030. I will work with my project partners to map modern slavery at local, national and international levels. This fellowship has the potential to save lives and show how computational statistics can advance progress towards the Sustainable Development Goals. By leveraging support from my project partners, I will influence politicians and policy makers to use my results to safeguard victims and prevent potential victims from suffering from modern slavery abuses.

如果我们要在 2030 年实现联合国可持续发展目标，我们需要开发更好的统计方法来绘制弱势群体的患病率。在这项奖学金中，我将 A. 对隐藏人口的有效计算统计方法进行基础研究，B. 使用这些方法在地方、国家和国际层面绘制现代奴隶制地图，以及 C. 与我的项目合作伙伴合作，改变基于政策的政策关于我们的循证研究。为了实现可持续发展目标，我们需要衡量我们距离实现这些目标有多远，量化谁最需要支持，并评估干预措施在创造可持续发展方面的成功程度。以现代奴隶制的受害者为例。受害者往往被边缘化和隐藏，虐待行为得不到举报和监控。估计有多少受害者、虐待发生的地点以及评估支持受害者的​​干预措施的有效性仍然是现代奴隶制和更广泛的可持续发展领域的挑战。有关受害者和虐待行为的数据通常是嘈杂的、质量差的或根本没有被收集。计算统计学的发展在这里非常强大。他们将提供一个框架来处理质量差和缺失的数据，同时避免对滥用行为如何发生进行具体和武断的假设。目前的方法要求研究人员对他们正在建模的滥用行为做出具体假设，而这些假设很难从数据中证明是合理的。我开发的方法将摆脱这一点，而是做出更一般的数学假设。这将使数据不言自明，并能提供更好的反事实证据和更现实的结论。为了实现这一目标，我在开发这些流行病方法方面拥有良好的记录，事实证明，当数据质量较差时，我的方法可以减少对特定假设的需求。然而，这种灵活性的代价是更大的计算负担、结果的不确定性增加以及使用这些方法时对技术专业知识的要求。为了加快实现可持续发展目标的进程，研究人员需要可以在实践中使用的方法。我将领导有效的计算统计方法的开发。通过减少计算负担、提供处理结果不确定性的机制以及使方法易于实施，它们将对非统计学家变得更具吸引力。我已经展示了我的发展如何能够大大减轻绘制贫困图时的数据收集负担，使这些方法对从事减贫工作的研究和组织更具吸引力。该奖学金的一个关键部分是与一名研究软件工程师合作，他可以​​开发其他研究人员和组织可以用来实施我的方法的数据系统和软件。我将用我的方法解决现代奴隶制中的紧迫问题，并推动这一领域实现联合国到 2030 年终结奴隶制的目标。我将与我的项目合作伙伴合作，在地方、国家和国际层面绘制现代奴隶制地图。该奖学金有可能拯救生命，并展示计算统计如何推动可持续发展目标的进展。通过利用项目合作伙伴的支持，我将影响政治家和政策制定者利用我的研究结果来保护受害者并防止潜在受害者遭受现代奴隶制虐待。