Developing a Text-Based Measure of Humility in Inquiry Using Computational Grounded Theory

Sarah Bratt, Erin Leahey, Charles Gomez, Jina Lee, Yeaeun Kwon, Charles Lassiter

Research output: Contribution to journalArticlepeer-review

Abstract

We describe a project in which we develop a text-based measure of HI in the context of scholarly communication using corpora of scientific publications. The data and analytic approach we use will circumvent known concerns with self-reported data on humility levels and will be calculable on a large scale. We use a computational grounded theory approach to develop a text-based measure of HI. We draw from an annotated corpus of scientific articles in economics, psychology, and sociology (2010–2023), generating three supra-dimensions of HI (Epistemic, Rhetorical, and Transparent) and several novel sub-codes of HI. We present our initial analysis with a focus on the three dimensions of HI derived from a computational grounded theory approach. The text-based measure helps us better understand how contextual factors shape HI and contribute to mixed methods in information science research.

Original languageEnglish (US)
Pages (from-to)855-857
Number of pages3
JournalProceedings of the Association for Information Science and Technology
Volume61
Issue number1
DOIs
StatePublished - Oct 2024
Externally publishedYes

Keywords

  • humility in inquiry
  • machine learning
  • NLP
  • Science of science
  • text-based measure

ASJC Scopus subject areas

  • General Computer Science
  • Library and Information Sciences

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