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

Examining Analytic Practices in Latent Dirichlet Allocation Within Psychological Science: Scoping Review

BACKGROUND

Topic modeling approaches allow researchers to analyze and represent written texts. One of the commonly used approaches in psychology is latent Dirichlet allocation (LDA), which is used for rapidly synthesizing patterns of text within "big data," but outputs can be sensitive to decisions made during the analytic pipeline and may not be suitable for certain scenarios such as short texts, and we highlight resources for alternative approaches. This review focuses on the complex analytical practices specific to LDA, which existing practical guides for training LDA models have not addressed.

OBJECTIVE

This scoping review used key analytical steps (data selection, data preprocessing, and data analysis) as a framework to understand the methodological approaches being used in psychology research using LDA.

METHODS

A total of 4 psychology and health databases were searched. Studies were included if they used LDA to analyze written words and focused on a psychological construct or issue. The data charting processes were constructed and employed based on common data selection, preprocessing, and data analysis steps.

RESULTS

A total of 68 studies were included. These studies explored a range of research areas and mostly sourced their data from social media platforms. Although some studies reported on preprocessing and data analysis steps taken, most studies did not provide sufficient detail for reproducibility. Furthermore, the debate surrounding the necessity of certain preprocessing and data analysis steps is revealed.

CONCLUSIONS

Our findings highlight the growing use of LDA in psychological science. However, there is a need to improve analytical reporting standards and identify comprehensive and evidence-based best practice recommendations. To work toward this, we developed an LDA Preferred Reporting Checklist that will allow for consistent documentation of LDA analytic decisions and reproducible research outcomes.

Rewealthization in twenty-first century Western countries: the defining trend of the socioeconomic squeeze of the middle class

The wealth-to-income ratio (WIR) in many Western countries, particularly in Europe and North America, increased by a factor of two in the last three decades. This represents a defining empirical trend: a rewealthization (from the French repatrimonialisation)-or the comeback of (inherited) wealth primacy since the mid-1990s. For the sociology of social stratification, "occupational classes" based on jobs worked must now be understood within a context of wealth-based domination. This paper first illustrates important empirical features of an era of rising WIR. We then outline the theory of rewealthization as a major factor of class transformations in relation to regimes stabilized in the post-WWII industrial area. Compared to the period where wealth became secondary to education and earnings for middle-class lifestyles, rewealthization steepens society's vertical structure; the "olive-shaped" Western society is replaced by a new one where wealth "abundance" at the top masks social reproduction and frustrations below.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40711-020-00135-6.

Diversity and Political Leaning: Considerations for Epidemiology

The positive effects of increased diversity and inclusion in scientific research and practice are well documented. In this issue, DeVilbiss et al. (Am J Epidemiol. 2020;189(10):998-1010) present findings from a survey used to collect information to characterize diversity among epidemiologists and perceptions of inclusion in the epidemiologic profession. They capture identity across a range of personal characteristics, including race, gender, socioeconomic background, sexual orientation, religion, and political leaning. In this commentary, we assert that the inclusion of political leaning as an axis of identity alongside the others undermines the larger project of promoting diversity and inclusion in the profession and is symptomatic of the movement for "ideological diversity" in higher education. We identify why political leaning is not an appropriate metric of diversity and detail why prioritizing ideological diversity counterintuitively can work against equity building initiatives. As an alternative to ideological diversity, we propose that epidemiologists take up an existing framework for research and practice that centers the voices and perspectives of historically marginalized populations in epidemiologic work.

Mapping the Academic Landscape of the Renewable Energy Field in Electrical and Electronic Disciplines

Research on renewable energy fields of electrical and electronic disciplines is key to promoting the efficient production and utilization of renewable energy, but its branches are numerous and development is uneven. This article examines the topic distribution and future development trajectory of this field, and aims to provide academic researchers with the clearest development context and organizational structure in this field. This study obtained all fields from 3743 articles in the field of renewable energy from the Web of Science (WoS) database, with a time span of 1992–2018. We applied statistical analysis, the latent dirichlet allocation (LDA) topic model, and the autoregressive integrated moving average (ARIMA) model to map topic landscapes in the field of renewable energy. By analyzing these fields, we discovered the digital characteristics of the field and divided the field into 29 different topics, such as “Power conversion technology”, “Micro-grid”, and “Electric vehicles and hybrid electric vehicles”, and analyzed the growth characteristics of topics in two time periods—1992–2005 and 2005–2018. Finally, based on the development trajectory of each topic, we predicted their future development enthusiasm, which was divided into cold, hot, and stable. We compiled statistics on the most popular outlets and citations for each topic, making it easy for researchers and journal editors to appreciate and apply them.

Mapping the Scientific Research on Healthcare Workers' Occupational Health: A Bibliometric and Social Network Analysis

In the last few years, the occupational health (OH) of healthcare workers (HCWs) has been shown increasing concern by both health departments and researchers. This study aims to provide academics with quantitative and qualitative analysis of healthcare workers’ occupational health (HCWs+OH) field in a joint way. Based on 402 papers published from 1992 to 2019, we adopted the approaches of bibliometric and social network analysis (SNA) to map and quantify publication years, research area distribution, international collaboration, keyword co-occurrence frequency, hierarchical clustering, highly cited articles and cluster timeline visualization. In view of the results, several hotspot clusters were identified, namely: physical injuries, workplace, mental health; occupational hazards and diseases, infectious factors; community health workers and occupational exposure. As for citations, we employed document co-citation analysis to detect trends and identify seven clusters, namely tuberculosis (TB), strength training, influenza, healthcare worker (HCW), occupational exposure, epidemiology and psychological. With the visualization of cluster timeline, we detected that the earliest research cluster was occupational exposure, then followed by epidemiology and psychological; however, TB, strength training and influenza appeared to gain more attention in recent years. These findings are presumed to offer researchers, public health practitioners a comprehensive understanding of HCWs+OH research.