Gender Representation In Sentence Examples Of The Merriam-Webster Advanced Learner’s English Dictionary

With the vigorous development of feminist movements in the world,the study of gender in language has become one of the hot topics in academia.The study of gender in dictionaries started in the 1960 s,and researchers have published dozens of papers.However,only few of them were quantitative studies which all have shortcomings in the research design.To make up for the problems of the previous studies,this study adopts a corpus-based research method to investigate the gender representation in the Merriam-Webster Advanced Learner’s English Dictionary(hereafter referred to as MWALED)from a critical lexicographic perspective.The study has constructed a specialized corpus of example sentences in MWALED which contains sentence examples with gender-related words as subjects in half of the volume of this dictionary.The corpus consists of two sub-corpora covering male-related and female-related example sentences,respectively.Then,a combination of manual analysis and automatic software analysis was used to analyze these two subcorpora.In this study,the manual lexical analysis has been conducted for proper nouns in the subjects of sentences in both sub-corpora,and the function of lexical-semantic analysis in LIWC has been adopted for common nouns or noun phrases in the subjects of sentences.Besides,for the textual contents of the two sub-corpora,this study has adopted the functions of discourse semantic analysis in LIWC,keyword list generation in AntConc,and transitivity as well as modality analysis in UAM.The study has shown that 1)there is a lack of gender diversity in MWALED,and only subjects referring to males and females,such as he and she,are present in the selfconstructed example sentence corpus of this study,while no expressions such as the third person singular usage of “they” referring to non-binary genders like gender fluidity are found in the corpus;2)the dictionary has gender biases since the example sentences for males are significantly more than those for females(the proportion of the number of example sentences for males exceeds that of females by 10%),and the number of male social roles portrayed in the dictionary is also significantly greater than that of women(19 times as many roles for men as for women);3)both males and females exhibit stereotypes shaped and imposed by the social environment over time,for example,men in this dictionary present strong,athletic,ambitious,and successful images whereas women are portrayed as people who are weak,concerned with appearance,emotional,and wives or mothers.The analysis of this study reveals that the lexicographers’ implicit attitudes and the long-established gender inequality in society are the main reasons for these problems in MWALED.In comparison to the relevant studies,this study is among the first to use various data analysis tools for a more accuracy research of the corpus,hoping to provide methodological help for future related research.Besides,this study,attempting to improve the gender balance and avoid gender biases and stereotypes in dictionary compilation,also helps to optimize the examples in the dictionary and improve the quality of the dictionary compilation.