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Assessing the Potential and Limits of Large Language Models in Qualitative Coding · Study

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Assessing the Potential and Limits of Large Language Models in Qualitative Coding

Xiner Liu, Jiayi Zhang, Amanda Barany, Maciej Pankiewicz, Ryan S. Baker

2024-01-01otherCommunications in computer and information science30 citationsclosed

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Computational and Text Analysis Methods Qualitative Research Methods and Applications Artificial Intelligence in Law

Abstract

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Citations in this library

Stored only when both papers are in the corpus — 30 citations exist upstream.

References

1994Qualitative Data Analysis: An Expanded Sourcebook

2023Exploring the Use of Artificial Intelligence for Qualitative Data Analysis: The Case of ChatGPT

2023Supporting Qualitative Analysis with Large Language Models: Combining Codebook with GPT-3 for Deductive Coding

2024An Examination of the Use of Large Language Models to Aid Analysis of Textual Data

2023CoAIcoder: Examining the Effectiveness of AI-assisted Human-to-Human Collaboration in Qualitative Analysis

2010Machine learning and rule‐based automated coding of qualitative data

2023From nCoder to ChatGPT: From Automated Coding to Refining Human Coding

2024ChatGPT for Education Research: Exploring the Potential of Large Language Models for Qualitative Codebook Development

Cited by

2025From Voices to Validity: Leveraging Large Language Models (LLMs) for Textual Analysis of Policy Stakeholder Interviews

2025Optimizing Generative AI's Accuracy and Transparency in Inductive Thematic Analysis: A Human-AI Comparison

Details

Year2024

Typeother

VenueCommunications in computer and information science

Languageen

Files0 public / 0

Identifiers

doi10.1007/978-3-031-76335-9_7

openalex_workW4403970100

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