The research topic landscape in the literature of social class and inequality.

Liang Guo,Lei Yin,Ariane Gorson-Deruel,Shikun Li,Lawrence King,Ruodan Lu

doi:10.1371/journal.pone.0199510

Abstract

The literature of social class and inequality is not only diverse and rich in sight, but also complex and fragmented in structure. This article seeks to map the topic landscape of the field and identify salient development trajectories over time. We apply the Latent Dirichlet Allocation topic modeling technique to extract 25 distinct topics from 14,038 SSCI articles published between 1956 to 2017. We classified three topics as “hot”, eight as “stable” and 14 as “cold”, based on each topic’s idiosyncratic temporal trajectory. We also listed the three most cited references and the three most popular journal outlets per topic. Our research suggests that future effort may be devoted to Topics “urban inequalities, corporate social responsibility and public policy in connected capitalism”, “education and social inequality”, “community health intervention and social inequality in multicultural contexts” and “income inequality, labor market reform and industrial relations”.

Highlights

Social stratification or social class refers to visible societal layers or classes of differing wealth, income, race, education or power [1]
Social class and social inequality are often used interchangeably, all of which are the products of an unequally structured society in which identities are socially produced on a large scale [2]
Recent so-called “black swan events” (i.e. Donald Trump’ victory in the American election and the Brexit referendum) and the growth of populism in Europe are the vivid examples of how human society is transformed by the struggle between different social classes

Summary

Introduction

Social stratification or social class refers to visible societal layers or classes of differing wealth, income, race, education or power [1]. Backed by Bayesian statistics, Latent Dirichlet Allocation (LDA) is developed to apply a probabilistic model to analyze word distributions in text documents and uncover topics in an automated fashion [7,11]. This generative modeling technique does not require prior categorization, labelling and annotation of the texts but reveals the invisible, latent topic structure through statistical procedures [12]. Note that LDA is a mix-membership model, which means that each document is represented as a mixture of a set of topics and each topic is regarded as a distribution over the words in the vocabulary [26]. Each student read 20 randomly-chosen articles and

24 Developmental psychology and parents’ child-rearing values and practices

Findings

Discussion and conclusions