Academic tutoring as a supplementary form of education has quickly spread across China. Tutoring has attracted young college-educated practitioners and has become a highly organized industry. The industry is fast-churning and opportunistic. This article examines the nature of tutors’ work using the qualitative data of 31 tutors and their managers in Beijing. Characteristics of tutoring are typical of precarious service labor with irregular work schedules, blurred work–life boundaries, and artificial emotional labor. However, we also found that tutors’ work was constrained by their invisible neighbor, schoolteachers, in a linked professional ecology. Tutors have limited jurisdiction over daily tasks and perform reactionary teaching based on school subjects. This lack of control has allowed the industry to rationalize their work, making tutors disposable. Our research offers new insights into the linked professions of tutors and schoolteachers. It enriches the concept of precarious and opportunistic labor practices from the sociology of professions perspective.

Analysts of discrete data often face the challenge of managing the tendency of inflation on certain values. When treated improperly, such phenomenon may lead to biased estimates and incorrect inferences. This study extends the existing literature on single-value inflated models and develops a general framework to handle variables with more than one inflated value. To assess the performance of the proposed maximum likelihood estimator, we conducted Monte Carlo experiments under several scenarios for different levels of inflated probabilities under multinomial, ordinal, Poisson, and zero-truncated Poisson outcomes with covariates. We found that ignoring the inflations leads to substantial bias and poor inference of the inflations—not only for the intercept(s) of the inflated categories but other coefficients as well. Specifically, higher values of inflated probabilities are associated with larger biases. By contrast, the generalized inflated discrete models (GIDMs) perform well with unbiased estimates and satisfactory coverages even when the number of parameters that need to be estimated is quite large. We showed that model fit criteria, such as Akaike information criterion, could be used in selecting the appropriate specifications of inflated models. Lastly, the GIDM was implemented using large-scale health survey data as a comparison to conventional modeling approaches such as various Poisson and Ordered Logit models. We showed that the GIDM fits the data better in general. The current work provides a practical approach to analyze multimodal data that exists in many fields, such as heaping in self-reported behavioral outcomes, inflated categories of indifference and neutral in attitude surveys, large amounts of zero, and low occurrences of delinquent behaviors.

This study examined a new form of pervasive gender inequality: the gender gap among high achievers in math and considered its implication for developing STEM talents. Using the cross-nation Programme for International Student Achievement (PISA) data from both 2003 and 2012, we examined the mathematics gender gap among 15-year-old high achievers across ten countries/regions. We showed a consistent male advantage among the top performers in mathematics. Follow-up regression analyses revealed that the gap was associated with some socio-demographic and schooling/attitudinal variables, even after controlling for the background variables. We argue that education communities should acknowledge and address this form of gender inequality, as it could have ramifications for the science, technology, engineering, and mathematics (STEM) education pipeline. Educators and society in general still face challenges in closing the gender gap among high achievers in math with an aim to develop a gender-balanced STEM talent pool.