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GENDER NEUTRALIZATION PROCESS IN CONTEMPORARY ENGLISH INTERNET DISCOURSE


pdfIlona M. Derik

PhD in Philology, Associate Professor, Head of the Chair of Translation, Theoretical and Applied Linguistics, State Institution «South Ukrainian National Pedagogical University named after K. D. Ushynsky», Odesa, Ukraine
e-mail: ilonaderik@gmail.com
ORCID ID: https://orcid.org/0000-0002-8979-4745

Alexander I. Iliadi

Doctor of philological sciences, Professor of the Chair of Translation, Theoretical and Applied Linguistics, State Institution «South Ukrainian National Pedagogical University named after K. D. Ushynsky», Odessa, Ukraine
e-mail: alexandr.iliadi@gmail.com
ORCID ID: https://orcid.org/0000-0001-5078-8316

DOI: https://doi.org/10.24195/2616-5317-2024-38.1


SUMMARY

In the focus of the article there is the problem of gender neutralization phenomenon in the modern English language in general and in contemporary English Internet discourse in particular. The changes in the English language in diachrony are studied and interpreted in close connection with the review of «political correctness» and general tendency of euphemizing acute issues in the social life. The research was chiefly performed on the basis of the British National Corpus and the samples of English Internet discourse with the total amount of the analyzed texts about 4000. Therefore, the findings of the research can be fully regarded as trust-worthy and highly informative. The results have proved the slow-down in the process of replacing gender marked job titles by gender-neutral words. The research has perspectives for the future. The perspective may be seen in employing other corpora. The chief purpose is to prevent any form of linguistic bias that may occur in Internet discourse. The comparative analysis of these processes in British, American, Australian and Canadian English might also be noteworthy. Nevertheless, the diversity and profoundness of the researches in this field have proved the urgency of the topic involved.


Key words: gender neutralization, job titles, English Internet discourse, political correctness, corpus.


REFERENCES

Allan K., Burridge K. (2006). Forbidden words: Taboo and the censoring of language. Cambridge : Cambridge University Press.
Baker P. (2006). Using corpora in discourse analysis. London : Continuum.
Bolukbasi T., Chang Kai-Wei, Zou J., Saligrama V. (2016). Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings.
NIPS’16: Proceedings of the 30th International Conference on Neural Information Processing Systems. December, pp. 4356–4364. URL: https://www.researchgate.net/
publication/305615978_Man_is_to_Computer_Programmer_as_Woman_is_to_Homemaker_Debiasing_Word_Embeddings
Bovin M. (2016). Occupational titles and supposed gender-neutrality: A corpusbased
diachronic study on gender-neutral occupational titles in American English [Department of English, Stockholms Universitet]. URL: http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A934656&dswid=5541.
Bucholtz M. (2014). The feminist foundations of language, gender, and sexuality research’. The handbook of language, gender, and sexuality, 2nd edn [eds Ehrlich S.,
Meyerhoff M., Holmes J.]. Oxford : Wiley-Blackwell, pp. 23–47.
Caliskan A., Bryson Joanna J., Narayanan A. (2017). Semantics derived automatically from languagecorpora contain human-like biases. URL: https://www.researchgate.net/publication/316973825_Semantics_derived_automatically_from_language_corpora_contain_human-like_biases
Cheshire J. (2008). Still a gender-biased language? English Today, 24(1), pp. 7–10.
Collins English Dictionary. URL: https://www.collinsdictionary.com/dictionary/ English. Retrieved 21 March 2024.
Ethayarajh K., Duvenaud D., Hirst G. (2019). Understanding Undesirable Word Embedding Associations. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy, pp. 1696–1705.
URL: https://aclanthology.org/P19-1166/
Hansen C. (2022). Dismantling or perpetuating gender stereotypes. The case of trans rights in the European court of human rights’ jurisprudence. URL: https://institucional.us.es/binasex/wp-content/uploads/2022/09/8-7022-Article-Text-40101-1-10-20220613.pdf
Kaneko M., Bollegala D. (2019). Gender-preserving Debiasing for Pre-trained Word Embeddings. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy, pp. 1641–1650. URL: https://aclanthology.org/P19-1160/
Leech G. (2011). Current Changes in English Grammar in Relation to Society. URL: https://www.youtube.com/watch?v=OtH5PpgkgJU. Retrieved March 22, 2024.
Musto C., Semeraro G., Lops P., Gemmis Marco de (2015). CrowdPulse: A framework for real-time semantic analysis of social streams. Information Systems,
Vol. 54, pp. 127–146.
Sczesny S., Formanowicz M., & Moser F. (2016). Can Gender-Fair Language Reduce Gender Stereotyping and Discrimination? Frontiers in Psychology, Vol. 7, Article 25, pp. 1–11.
The British National Corpus (2001). Version 2 (BNC World). Distributed by Oxford University Computing Services on behalf of the BNC Consortium. URL: http://www.natcorp.ox.ac.uk/ Retrieved March 22, 2024.