Stylistic and linguistic variations in compliments: an empirical analysis of children’s gender schema development with machine learning algorithms
Abstract As hypothesized by Bem (1981)’s Gender Schema Theory, individuals regulate themselves and their expectations towards others according to the gender norms in a community. The current study examines children’s gender schema regarding the language styles in compliments addressed to both the ge...
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Format: | Article |
Language: | English |
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Springer Nature
2023-04-01
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Series: | Humanities & Social Sciences Communications |
Online Access: | https://doi.org/10.1057/s41599-023-01648-4 |
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author | Xinyu Liao Yanhui Zhang |
author_facet | Xinyu Liao Yanhui Zhang |
author_sort | Xinyu Liao |
collection | DOAJ |
description | Abstract As hypothesized by Bem (1981)’s Gender Schema Theory, individuals regulate themselves and their expectations towards others according to the gender norms in a community. The current study examines children’s gender schema regarding the language styles in compliments addressed to both the gendered self and others. Two types of oral discourse completion tasks were designed for the purpose, where twenty-five Mandarin-speaking children were instructed to pay compliments in a normal-speaking style and an imitated style of the opposite gender. Machine learning algorithms were implemented to analyze the variations of language features at lexical, discourse-pragmatic, and discourse-semantic levels. The results show that, compared to lexical features such as lexical richness and word choices, discourse-pragmatic features are more prone to gender ideologies and exhibit style-shifting in children’s imitation of the opposite sex when addressing compliments. At the discourse-semantic level, a significantly low probability of positivity was demonstrated in girls’ imitated compliments, according to the results of the logistic regression. In general, the findings support the presence of gender-differentiated language styles among pre-adolescent children. In particular, girls at this age have developed the stereotype that boys tend to use language with a less prosocial sentiment for the manifestation of their “maleness”. Directions for improving the experimental design and uncovering the possible confounding mechanisms were discussed to illuminate the multidimensional complexity of the cross-gender variations in the more nuanced speech traits, such as the use of intensifiers. |
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format | Article |
id | doaj.art-0fb5459571804ece89cdca7ff8690bbb |
institution | Directory Open Access Journal |
issn | 2662-9992 |
language | English |
last_indexed | 2024-04-09T18:55:37Z |
publishDate | 2023-04-01 |
publisher | Springer Nature |
record_format | Article |
series | Humanities & Social Sciences Communications |
spelling | doaj.art-0fb5459571804ece89cdca7ff8690bbb2023-04-09T11:10:51ZengSpringer NatureHumanities & Social Sciences Communications2662-99922023-04-0110111210.1057/s41599-023-01648-4Stylistic and linguistic variations in compliments: an empirical analysis of children’s gender schema development with machine learning algorithmsXinyu Liao0Yanhui Zhang1University of OxfordUniversity of Nottingham Ningbo ChinaAbstract As hypothesized by Bem (1981)’s Gender Schema Theory, individuals regulate themselves and their expectations towards others according to the gender norms in a community. The current study examines children’s gender schema regarding the language styles in compliments addressed to both the gendered self and others. Two types of oral discourse completion tasks were designed for the purpose, where twenty-five Mandarin-speaking children were instructed to pay compliments in a normal-speaking style and an imitated style of the opposite gender. Machine learning algorithms were implemented to analyze the variations of language features at lexical, discourse-pragmatic, and discourse-semantic levels. The results show that, compared to lexical features such as lexical richness and word choices, discourse-pragmatic features are more prone to gender ideologies and exhibit style-shifting in children’s imitation of the opposite sex when addressing compliments. At the discourse-semantic level, a significantly low probability of positivity was demonstrated in girls’ imitated compliments, according to the results of the logistic regression. In general, the findings support the presence of gender-differentiated language styles among pre-adolescent children. In particular, girls at this age have developed the stereotype that boys tend to use language with a less prosocial sentiment for the manifestation of their “maleness”. Directions for improving the experimental design and uncovering the possible confounding mechanisms were discussed to illuminate the multidimensional complexity of the cross-gender variations in the more nuanced speech traits, such as the use of intensifiers.https://doi.org/10.1057/s41599-023-01648-4 |
spellingShingle | Xinyu Liao Yanhui Zhang Stylistic and linguistic variations in compliments: an empirical analysis of children’s gender schema development with machine learning algorithms Humanities & Social Sciences Communications |
title | Stylistic and linguistic variations in compliments: an empirical analysis of children’s gender schema development with machine learning algorithms |
title_full | Stylistic and linguistic variations in compliments: an empirical analysis of children’s gender schema development with machine learning algorithms |
title_fullStr | Stylistic and linguistic variations in compliments: an empirical analysis of children’s gender schema development with machine learning algorithms |
title_full_unstemmed | Stylistic and linguistic variations in compliments: an empirical analysis of children’s gender schema development with machine learning algorithms |
title_short | Stylistic and linguistic variations in compliments: an empirical analysis of children’s gender schema development with machine learning algorithms |
title_sort | stylistic and linguistic variations in compliments an empirical analysis of children s gender schema development with machine learning algorithms |
url | https://doi.org/10.1057/s41599-023-01648-4 |
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