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Why and when entrepreneurs with calling perform better? The effects of calling and money motivation on entrepreneurial performance
Although calling is frequently stated in entrepreneurial practice, empirical study examining the impact of calling on entrepreneurial activities is scarce. Based on self-regulation theory, two-wave survey is administered among 174 Chinese entrepreneurs to investigate the influence of calling on entrepreneurial performance. The results indicate that calling has a significant and positive impact on entrepreneurial performance of entrepreneurs. Innovative behavior mediates the positive association between calling and entrepreneurial performance. Money motivation negatively moderates the indirect effect of calling on entrepreneurial performance via innovative behavior. Precisely, the positive effect of calling on entrepreneurial performance via innovative behavior is stronger at low level of money motivation. This study theoretically enriches the understanding of factors driving entrepreneurial performance and extends the application of calling to the entrepreneurial field.
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.
The evolution of preferred male traits, female preference and the G matrix: “Toto, I’ve a feeling we’re not in Kansas anymore”
Female preference exerts selection on male traits. How such preferences affect male traits, how female preferences change and the genetic correlation between male traits and female preference were examined by an experiment in which females were either mated to males they preferred (S lines) or to males chosen at random from the population (R lines). Female preference was predicted to increase the time spent calling by males. Thirteen other song components were measured. Preference for individual traits was greatest for time spent calling(CALL), volume(VOL) and chirp rate(CHIRP) but the major contributors in the multivariate function were CALL and CHIRP, the univariate influence of VOL arising from correlations to these traits. Estimation of β, the standardized selection differential, for CALL resulting from female preference showed that it was under strong direct selection. However, contrary to prediction, CALL did not change over the course of the experiment whereas VOL, CHIRP and other song components did. Simulation of the experiment using the estimated G matrix showed that lack of change in CALL resulted from indirect genetic effects negating direct effects. Changes in song components were largely due to indirect effects. This experiment showed that female preference may exert strong selection on traits but how they respond to such selection will depend greatly upon the G matrix. As predicted, female preference declined in the R lines. The genetic correlations between preference and preferred traits did not decline significantly more in the R lines, suggesting correlations resulted from both linkage disequilibrium and pleiotropy.
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.
Comparative evaluation of SNVs, indels, and structural variations detected with short- and long-read sequencing data
Short- and long-read sequencing technologies are routinely used to detect DNA variants, including SNVs, indels, and structural variations (SVs). However, the differences in the quality and quantity of variants detected between short- and long-read data are not fully understood. In this study, we comprehensively evaluated the variant calling performance of short- and long-read-based SNV, indel, and SV detection algorithms (6 for SNVs, 12 for indels, and 13 for SVs) using a novel evaluation framework incorporating manual visual inspection. The results showed that indel-insertion calls greater than 10 bp were poorly detected by short-read-based detection algorithms compared to long-read-based algorithms; however, the recall and precision of SNV and indel-deletion detection were similar between short- and long-read data. The recall of SV detection with short-read-based algorithms was significantly lower in repetitive regions, especially for small- to intermediate-sized SVs, than that detected with long-read-based algorithms. In contrast, the recall and precision of SV detection in nonrepetitive regions were similar between short- and long-read data. These findings suggest the need for refined strategies, such as incorporating multiple variant detection algorithms, to generate a more complete set of variants using short-read data.
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