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This chapter validates the grounded theory of whataboutery in the UN Security Council using three lenses: machine learning, practitioner interviews, and field observation. A locally trained GloVe model, checked against pre-trained GloVe and GPT embeddings, shows the categories are both consistent and distinct via high within-category cosine similarity and clear distance from random corpora. Exploratory NLP locates UNSC rhetoric between forensic, epideictic, and deliberative styles, and charts ‘double standards’ dynamics and Russia–West verbal conflict. Interviews and chamber observations corroborate performative, looping exchanges, heavier P5 use of whataboutisms and tu quoques, and productivity behind closed doors. The chapter synthesises validations and opens up a discussion of the effects of whataboutery in the UN Security Council.

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Validating the Theory of Whataboutery: Machine Learning, Diplomats, and Observations

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  • 发表日期：

    2025年

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  • Dmitry Zavialov

摘要

This chapter validates the grounded theory of whataboutery in the UN Security Council using three lenses: machine learning, practitioner interviews, and field observation. A locally trained GloVe model, checked against pre-trained GloVe and GPT embeddings, shows the categories are both consistent and distinct via high within-category cosine similarity and clear distance from random corpora. Exploratory NLP locates UNSC rhetoric between forensic, epideictic, and deliberative styles, and charts ‘double standards’ dynamics and Russia–West verbal conflict. Interviews and chamber observations corroborate performative, looping exchanges, heavier P5 use of whataboutisms and tu quoques, and productivity behind closed doors. The chapter synthesises validations and opens up a discussion of the effects of whataboutery in the UN Security Council.