JOURNAL ARTICLE
Japanese turn-initial particle hai in instruction-compliance sequences in boxing.
Published In: Interactional Linguistics, 2025, v. 5, n. 1. P. 127 1 of 3
Database: Humanities Source Ultimate 2 of 3
Authored By: Okada, Misao 3 of 3
Abstract
This paper analyzes how language and body interact in boxing sparring sessions by focusing on the Japanese particle hai (lit. 'yes') as it occurs turn-initially in the first part of instruction-compliance sequences. Based on sequential and embodied analysis of 11 boxing sparring sessions, this paper examines: (1) in what sequential and embodied environments hai is used; (2) if hai responds to a focal moment, what constitutes that moment; (3) what actions do hai-prefaced instructions indicate? How do language and body interact when these actions emerge? This paper identifies three environments: (1) while a boxer is being attacked, the particle prefaces instruction to evade the attack; (2) after a first phase of combined boxing movements, it precedes instruction pursuing the second phase; (3) after a change of distance, the particle introduces instructions for punches which are suitable at that distance. In each environment, hai is used to identify the exact moment at which targeted shifts from a current body alignment to a different one should be implemented. Depending on the temporal order of language and body, hai-prefaced instructions express different actions, e.g., 'late' instruction can "acknowledge" (Mondada 2021) boxer's independent initiations of the targeted action and, simultaneously, make their completions relevant. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Interactional Linguistics. 2025/01, Vol. 5, Issue 1, p127
- Document Type:Article
- Subject Area:Sports and Leisure
- Publication Date:2025
- ISSN:26664224
- DOI:10.1075/il.24011.oka
- Accession Number:187643332
- Copyright Statement:Copyright of Interactional Linguistics is the property of John Benjamins Publishing Co. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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