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Human–NLP collaboration made three dimensions of qualitative trustworthiness inspectable

Ha Nguyen, June Ahn, Ashlee Belgrave, Jiwon Lee, Lora Cawelti, Ha Eun Kim, Yenda Prado, Rossella Santagata, Adriana Villavicencio

2nd International Conference on Quantitative Ethnography (ICQE 2020)

How natural language processing combined with researcher interpretation can strengthen credibility, dependability, and confirmability in qualitative analysis. The case demonstrates practical affordances for efficient analysis while keeping human examination central to the claims and to the trustworthiness audit. A successful illustration does not make NLP output inherently credible, transferable, or unbiased; trustworthiness still depends on data quality, researcher reflexivity, validation, and transparent disagreement handling.

Le résumé vérifié de l'article est actuellement disponible en anglais.