Scholars employ two main measures of the executive constraints embedded in European Union laws: one is based on the variation in the use of different types of restrictions, and the second is based on the frequency of such use. They reflect two alternative conceptualizations of bureaucratic control. We label them, respectively, as the “toolbox perspective” and the “design perspective”. We illustrate that the constraint frequency measure poses fewer validity problems in estimating legislators' intent to constrain implementation and tends to produce less severe measurement errors. We then evaluate the performance in estimating constraint variation of a recent computational application and identify potential drawbacks of automated learning from hand-coded provisions. We lastly introduce a skeletal framework for a machine-learning approach based on the syntactic structures employed by legislators that could improve the performance of this innovative technique.

中文翻译：

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欧盟法律中从手工编码到机器学习的官僚约束的概念和措施

学者们采用两种主要措施来衡量欧盟法律中嵌入的行政限制：一是基于不同类型限制使用的变化，二是基于频率的这种用途。它们反映了官僚控制的两种可供选择的概念。我们分别将它们标记为“工具箱视角”和“设计视角”。我们表明，约束频率测量在估计立法者限制实施的意图时带来的有效性问题较少，并且往往会产生不太严重的测量误差。然后，我们评估最近计算应用程序的约束变化估计性能，并确定从手工编码的规定中进行自动学习的潜在缺点。最后，我们介绍了一个基于立法者所采用的句法结构的机器学习方法的骨架框架，可以提高这种创新技术的性能。