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The increasing number of university graduates and economic fluctuations have intensified employment competition in China. While various hiring barriers have been extensively studied, major-based employment thresholds remain underexplored. This study examines the distribution of major-based thresholds in the labor market and explores differences in expectations between higher education and the labor market.
Using computational text analysis, this study integrates word co-occurrence network analysis and word vector analysis to investigate employment alignment across different majors. The network analysis maps the relationships between majors and job positions in the Chinese labor market, as well as the competitive relationships among majors. It highlights significant variations in employment breadth, job relevance, and competitiveness, revealing a pyramidal employment structure—where a small number of majors (e.g., Computer Science, Finance) dominate job opportunities, while most majors face constraints in alignment and substitutability.
The word vector analysis quantifies differences in perceptions or expectations of majors between academia and the labor market. Applying the global anchor method, this study compares vector gaps between curriculum syllabi and job advertisements, ranking majors based on their alignment across contexts. Results show that the labor market’s expectations for Science and Engineering majors align more closely with higher education than those for Humanities and Social Sciences majors. Additionally, two cognitive biases are identified: (1) First-Type Bias, where majors appear similar in education but diverge in job market expectations, and (2) Second-Type Bias, where majors are distinct in education but treated similarly in hiring. The dominance of the second bias (462 out of 528 major pairs) underscores the labor market’s tendency to cluster majors in a coarse-grained manner.
These findings contribute to discussions on educational alignment with labor market demands, offering insights into improving university career guidance and employer hiring practices.