“Still There”: (Dis)engaging with Dickens's Minor Characters

Abstract

More can be done in minor character studies to account for the strong sense of being that emerges at the edges of the nineteenth-century novel. By pairing traditional readings of the minor character in narrative theory with sociologist Erving Goffman's writings on disengagement, this article offers a different perspective on the competition for narrative attention as we know it. For example, when disengagement is taken into account, Alex Woloch's losers in the competition for narrative attention become winners in the formulation of a fulfilling social life. Dickens's minor characters take part in central spaces while not being contained by them. Their distance from main scenes and settings, captured in passing by a gaze that has no interest in registering these elsewheres in any level of depth, has the effect of making minor characters appear strange, memorable, or other, even though their worlds are quite rich. But Dickens's minor characters define the ingenuity of counterintuition, pointing toward a suppressed energy that belies the flatness of a minor character. Drawn with care, these characters build alternative, codependent ways of surviving on the edges of the characterological field.

Character Representation and Application Analysis of English Language and Literature Based on Neural Network

“…Starkowski believes that in the study of literary works, more energy and attention should be devoted to the study of secondary characters. And taking Dickens' "Still There" as an example, he discusses the inadequacy of the traditional interpretation of secondary characters and the reflection of secondary characters in the novel on the society in the work [12]. In a study by Indrasari et al, taking Bronte's novel Wuthering Heights as an example, they used sociological methods and qualitative description methods to conduct an in-depth analysis of the middle-class female roles in the novel in the 19th century, thereby deepening the understanding of these roles [13].…”

“…Firstly, an improved genetic algorithm with adaptive mutation (AGA) is proposed to obtain the optimal parameters of BPNN. The adaptive mutation probability of AGA is calculated by formula (12).…”

“…In formula (12), M is the total number of the updates of the algorithm. m is the current iterative update number of the algorithm.…”