Since the proposal by Austin (1962) and Searle (1969), Speech Act Theory has become established as an important theoretical framework in linguistics and pragmatics. In this theory, speech acts are classified into three types: locutionary acts, illocutionary acts, and perlocutionary acts. Specific linguistic acts such as requests, apologies, invitations, and expressions of gratitude are theoretically explained in various texts and dictionaries as belonging to illocutionary acts. However, there exists a discrepancy between the definition of terminology and its actual usage, causing serious confusion for beginning students. This research aims to contribute to beginning students by reorganizing the current state and patterns of terminological usage and examining the relationship between superordinate and subordinate concepts.

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Imbalanced data distributions are prevalent in corpus linguistics, where sub-corpora frequently vary in size due to collection constraints or uneven population distributions. This study investigated the impact of class imbalance on text classification and conducted a comparative evaluation of undersampling, oversampling, SMOTE, and a cost-sensitive weighted generalized linear model using the Spam dataset. The experimental results demonstrate that as class imbalance intensifies, the overall accuracy remains misleadingly high, whereas precision and recall decline sharply, underscoring that reliance on accuracy alone yields distorted conclusions. Among the evaluated methods, SMOTE achieved the highest F1 score, and the weighted model provided a robust balance between predictive performance and data integrity. These findings suggest that corpus researchers should evaluate imbalanced classification tasks using precision, recall, and F1 scores and select a compensatory methodology that aligns with their specific analytical objectives.

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What should a brief introduction to statistics teach foreign language teachers and beginning researchers, and what can it leave out? Drawing on workshops conducted between 2013 and 2026, this article presents an introduction centered on descriptive statistics and data visualization. The 2026 workshop was primarily lecture-based, with a browser tool available for participants to explore the data as needed while listening. The article examines five choices concerning significance testing, the order of introducing distributions, fictional data, dedicated software, and visual interpretation. It sets out the reasons for these choices alongside objections that remain unresolved. The author also questions the basis for continuing his own teaching practice without systematically assessing its outcomes. By making these judgments explicit, the article invites colleagues to examine what introductory teaching should prioritize and how its outcomes should be evaluated.

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