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Learning Morality with Siblings: The Untold Tale of a Mid-Twentieth Century Taiwanese Family

Published online by Cambridge University Press:  21 December 2021

Jing Xu*
Affiliation:
University of Washington, USA
*
*Corresponding author. Email: jingxu1983@gmail.com
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Abstract

This article uses a new theoretical and methodological framework to reconstruct a story of two children from fieldnotes collected by anthropologists Arthur and Margery Wolf in rural Taiwan (1958 to 1960). Through the case of a brother–sister dyad, it examines the moral life of young children and provides a rare glimpse into sibling relationship in peer and family contexts. First, combining social network analysis and NLP text-analytics, this article introduces a general picture of these siblings’ life in the peer community. Moreover, drawing from naturalistic observations and projective tests, it offers an ethnographic analysis of how children support each other and assert themselves. It emphasizes the role of child-to-child ties in moral learning, in contrast to the predominant focus of parent–child ties in the study of Chinese families. It challenges assumptions of the Chinese “child training” model and invites us to take children's moral psychology seriously and re-discover their agency.

Information

Type
Research Article
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press
Figure 0

Figure 1 Child Observation entire networkNote: Analysis in Figures 1, 2, and 3 was all performed in Python programming language (packages: Pandas and NetworkX), and visualized in Gephi (layout: Force Atlas 2 algorithm).

Figure 1

Figure 2 Child Observation subset (Child #49 and #50) union network

Figure 2

Figure 3 Child Observation child network (modularity)

Figure 3

Table 1 Child #49 and #50 in child network. “Max*” represents the child/node who had the highest value in each of the three network attributes. It may not refer to a single child; it just serves as a reference value for positioning Child #49 and #50 in the network

Figure 4

Figure 4 Top 100 highest-frequency words in Child Observation subset (Child #49 and #50)

Figure 5

Figure 5 Top 50 highest-frequency words in Child Observation subset (Child #49 and #50)

Figure 6

Figure 6 Word co-occurrence network (top 22) in Child Observation subset (Child #49 and #50)Note: The word co-occurrence matrix in Figure 6 was computed in Python programming language, via word embeddings analysis, and then visualized in Gephi (layout: Force Atlas 2 algorithm).

Figure 7

Table 2 Behavioral grading results (Child #49 and #50). After grading all behaviors manually, I exported the behavioral grading data to and completed analysis in R programming language (package: dplyr)