128
Views
2
CrossRef citations to date
0
Altmetric
Articles

Sociolinguistic configuration of a regimented workforce: a study of the Nigerian army’s workout songs

ORCID Icon &
Pages 1635-1652 | Received 02 Jan 2023, Accepted 03 Apr 2023, Published online: 12 Apr 2023
 

ABSTRACT

This paper examines the sociolinguistic configuration of the Nigerian Army as indexed in the workout songs they used during jogging exercises in Calabar, South-eastern Nigeria. Data for the study were generated by means of participant observation and semi-structured interviews. The findings, drawing insights from Discourse community theory and multilingual identity concept, show that the workout songs bear (code mixed) elements of English, Nigerian Pidgin, indigenous languages, and military slanguage. The songs replicate the language practices in the Nigerian Army’s discourse community that describes the participants’ multilingual identities. Aside from serving as psychological devices for solidarity, social inclusion, and morale boosting for the regimented workforce, the songs provide insights into the use of institutional registers and slang to attend to communication exigencies in their social contexts. The code mixed songs as outcomes of multilingualism, also portray Nigerian Army as interethnolinguistic and multicultural workforce and define their identity, solidarity, and professional belonging.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 310.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.