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Skill dependencies uncover nested human capital
Modern economies require increasingly diverse and specialized skills, many of which depend on the acquisition of other skills first. Here we analyse US survey data to reveal a nested structure within skill portfolios, where the direction of dependency is inferred from asymmetrical conditional probabilities—occupations require one skill conditional on another. This directional nature suggests that advanced, specific skills and knowledge are often built upon broader, fundamental ones. We examine 70 million job transitions to show that human capital development and career progression follow this structured pathway in which skills more aligned with the nested structure command higher wage premiums, require longer education and are less likely to be automated. These disparities are evident across genders and racial/ethnic groups, explaining long-term wage penalties. Finally, we find that this nested structure has become even more pronounced over the past two decades, indicating increased barriers to upward job mobility.
Professional demand analysis for teaching Chinese to speakers of other languages: a text mining approach on internet recruitment platforms
The rapid development of international education in China highlights the growing importance of employment analysis in Teaching Chinese to Speakers of Other Languages (TCSOL). This study explores the enterprise demands for TCSOL professionals using text mining techniques to analyze recruitment data collected from four major platforms: Boss Zhipin, Zhaopin.com, 51job.com, and Liepin.com. Combining descriptive statistics, LDA topic modeling, BERT-BiLSTM-CRF-based named entity recognition, and co-occurrence network analysis were used. Results show that there is a high demand for TCSOL professionals, especially for small-scale enterprises located in first-tier cities such as Beijing, Shanghai, Guangzhou, and Shenzhen. Employers tend to favor candidates with at least a bachelor’s degree and 1–3 years of work experience. The topic model highlighted three central themes in job descriptions, emphasizing a shift toward a more diverse skill set. Named entity recognition identified essential attributes such as “communication ability”, “teaching experience”, “bachelor’s degree or above” and “responsibility” as core recruitment requirements. The co-occurrence network analysis revealed the importance of “teaching” and “priority” as core skill nodes. Time series analysis showed seasonal fluctuations in recruitment demand, peaking during spring recruitment and graduation periods. A hierarchical model of talent demand and development in TCSOL is proposed, integrating the perspectives of employers, job seekers, educators, and policymakers. This study provides valuable insights for aspiring TCSOL professionals, offering guidance to better align talent training with market needs and improve employment prospects.
Women in chemistry: Q&A with Professor Aurora J. Cruz-Cabeza
Why did you choose to be a scientist? I have always been very curious about the world and wanted to learn how things work. From…
A manifesto for a globally diverse, equitable, and inclusive open science
The field of psychology has rapidly transformed its open science practices in recent years. Yet there has been limited progress in integrating principles of diversity, equity and inclusion. In this Perspective, we raise the spectre of Questionable Generalisability Practices and the issue of MASKing (Making Assumptions based on Skewed Knowledge), calling for more responsible practices in generalising study findings and co-authorship to promote global equity in knowledge production. To drive change, researchers must target all four key components of the research process: design, reporting, generalisation, and evaluation. Additionally, macro-level geopolitical factors must be considered to move towards a robust behavioural science that is truly inclusive, representing the voices and experiences of the majority world (i.e., low-and-middle-income countries).
Evolution and impact of the science of science: from theoretical analysis to digital-AI driven research
The Science of Science (SoS) examines the mechanisms driving the development and societal role of science, evolving from its sociological roots into a data-driven discipline. This paper traces the progression of SoS from its early focus on the social functions of science to the current era, characterized by large-scale quantitative analysis and AI-driven methodologies. Scientometrics, a key branch of SoS, has utilized statistical methods and citation analysis to understand scientific growth and knowledge diffusion. With the rise of big data and complex network theory, SoS has transitioned toward more refined analyses, leveraging artificial intelligence (AI) for predictive modeling, sentiment annotation, and entity extraction. This paper explores the application of AI in SoS, highlighting its role as a surrogate, quant, and arbiter in advancing data processing, data analysis and peer review. The integration of AI has ushered in a new paradigm for SoS, enhancing its predictive accuracy and providing deeper insights into the internal dynamics of science and its impact on society.
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