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 language | English (US) |
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Pages (from-to) | 855-857 |
Number of pages | 3 |
Journal | Proceedings of the Association for Information Science and Technology |
Volume | 61 |
Issue number | 1 |
DOIs | |
State | Published - Oct 2024 |
Externally published | Yes |
Keywords
- humility in inquiry
- machine learning
- NLP
- Science of science
- text-based measure
ASJC Scopus subject areas
- General Computer Science
- Library and Information Sciences