Natural language processing word dictionaries for personnel selection: Achieving explainable, generalizable, and incremental prediction with low impact when passively scoring narrative application information.
Authors
Michael A. Campion (ORCID: https://orcid.org/0000-0001-7066-2788)
Emily D. Campion (ORCID: https://orcid.org/0000-0003-1555-2089)
Natural language processing word dictionaries for personnel selection: Achieving explainable, generalizable, and incremental prediction with low impact when passively scoring narrative application information.
Michael A. Campion, Emily D. Campion
Journal
Journal of Applied Psychology
Topics
AI and HR Technologies
article
Natural language processing word dictionaries for personnel selection: Achieving explainable, generalizable, and incremental prediction with low impact when passively scoring narrative application information.
Michael A. Campion, Emily D. Campion
article en
Abstract
No abstract available for this paper.
Publication Tag
Journal of Applied Psychology
Institutions
University of Iowa (US), Purdue University West Lafayette (US)
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Natural language processing word dictionaries for personnel selection: Achieving explainable, generalizable, and incremental prediction with low impact when passively scoring narrative application information. — Michael A. Campion, Emily D. Campion · Journal of Applied Psychology (2026) | TGRS Research Map | TGRS