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Understanding Delegation Through Machine Learning: A Method and Application to the European Union
7 references·6 citing·41 second hop
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this work references cited by second hop

References

  • 2013Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts3,290 cited
  • 2018quanteda: An R package for the quantitative analysis of textual data1,342 cited
  • 1999Delegating Powers1,054 cited
  • 2000Delegating powers: a transaction cost politics approach to policy making under separate powers910 cited
  • 2003Deliberate discretion?: the institutional foundations of bureaucratic autonomy904 cited
  • 2002Deliberate Discretion?899 cited
  • 2016Crowd-sourced Text Analysis: Reproducible and Agile Production of Political Data296 cited

Cited by

  • 2022Introduction to Neural Transfer Learning With Transformers for Social Science Text Analysis30 cited
  • 2022A Dynamic Measure of Bureaucratic Reputation: New Data for New Theory18 cited
  • 2023Rules as policy data? Measuring and linking policy substance and legislative context14 cited
  • 2025Toward a framework for creating trustworthy measures with supervised machine learning for text4 cited
  • 2021Neural Transfer Learning with Transformers for Social Science Text Analysis.3 cited
  • 2024Troubles in Text: Using Natural Language Processing to Recognize Government Rationalizations for Rights Abuses1 cited

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