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Information Retrieval for Evidence-Based Policy Making Applied to Lifelong Learning

机译:循证决策中的信息检索应用于终身学习

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Policy making involves an extensive research phase during which existing policies which are similar to the one under development need to be retrieved and analysed. This phase is time-consuming for the following reasons: (ⅰ) there is no unified format for policy documents; (ⅱ) there is no unified repository of policies; and (ⅲ) there is no retrieval system designed for querying any repositories which may exist. This creates an information overload problem for policy makers who need to be aware of other policy documents in order to inform their own. The goal of this work is to introduce a novel application area for studying information retrieval models: the information seeking phase of policy design, applied to life-long learning policy-making. In this paper, we address this problem by developing a common representation for policy documents, informed by domain experts, in order to facilitate their indexing and retrieval by users. This position paper highlights the research questions that we aim to answer in our future work and the dataset that we intend to use to do so. Our main contribution is the creation of a unified dataset of policy interventions which can be used for highly specialised information retrieval tasks, and will be released in order to provide the field with the first unified repository of policy interventions in adult education.
机译:政策制定涉及广泛的研究阶段,在此阶段需要检索和分析与正在制定的政策相似的现有政​​策。此阶段很耗时,原因如下:(ⅰ)政策文件没有统一的格式; (ⅱ)没有统一的政策资料库; (ⅲ)没有设计用于查询可能存在的任何存储库的检索系统。这给决策者带来了信息过载的问题,他们需要了解其他政策文件以告知自己的信息。这项工作的目的是介绍一个用于研究信息检索模型的新颖应用领域:策略设计的信息搜索阶段,该阶段应用于终身学习决策。在本文中,我们通过在域专家的指导下开发策略文件的通用表示法来解决此问题,以便于用户对其进行索引和检索。本立场文件重点介绍了我们希望在未来的工作中回答的研究问题以及打算用于此目的的数据集。我们的主要贡献是创建了政策干预措施的统一数据集,该数据集可用于高度专业化的信息检索任务,并将被发布以为该领域提供成人教育中第一个统一的政策干预措施资料库。

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