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Keeping Humans in the Loop: Human-Centered Automated Annotation with Generative AI
14 references·3 citing·38 second hop
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Investigating LLM-Assisted Annotation for Subjective Tasks (2025)Just Put a Human in the Loop?…A Coefficient of Agreement for Nominal Scales (1960)The Measurement of Observer Agreement for Categorical Data (1977)Content Analysis: An Introduction to its Methodology. 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References

  • 2019Content Analysis: An Introduction to Its Methodology16,055 cited
  • 2008Inter-Coder Agreement for Computational Linguistics1,691 cited
  • 2021Measurement and Fairness396 cited
  • 2023ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning296 cited
  • 2011Coder Reliability and Misclassification in the Human Coding of Party Manifestos250 cited
  • 2023Why open-source generative AI models are an ethical way forward for science104 cited
  • 2022Do AIs know what the most important issue is? Using language models to code open-text social survey responses at scale96 cited
  • 2023Using Large Language Models for Qualitative Analysis can Introduce Serious Bias75 cited
  • 2024How to Use Large-Language Models for Text Analysis73 cited
  • 2022Informing the Leader: Bureaucracies and International Crises68 cited
  • 2022Dynamics of cross-platform attention to retracted papers57 cited
  • 2023Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark32 cited
  • 2024The rise of and demand for identity‐oriented media coverage23 cited
  • 2020Uncertainty over Uncertainty: Investigating the Assumptions, Annotations, and Text Measurements of Economic Policy Uncertainty5 cited

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Cited by

  • 2026Human-in-the-Loop Artificial Intelligence: A Systematic Review of Concepts, Methods, and Applications50 cited
  • 2025Just Put a Human in the Loop? Investigating LLM-Assisted Annotation for Subjective Tasks36 cited
  • 2025Prompting the Machine: Introducing an LLM Data Extraction Method for Social Scientists9 cited