This article describes the use of dialogic teaching in a rural Tibetan school in China. Using ethnographic data from a school that serves socially disadvantaged Tibetan youth, we demonstrate that dialogic teaching is a powerful pedagogy even in a resource-deprived learning environment. We describe how oral commentary, interpretive discussions, and debate form the core of effective school practices. Inspired by traditional monastic training, schoolteachers have transformed the century-long indigenous oral practices into a culturally-appropriate form of pedagogy, infusing it into modern academic subjects. This article aims to enrich the growing literature on dialogic teaching by presenting a case outside Anglo-American societies. We examine an indigenous form of dialogic teaching by considering Alexander (2017, 2020)’s framework. Our analysis shows that while the specific pedagogy is developed indigenously, it is compatible with the dialogic philosophy. The findings suggest that the dialogic approach is a general pedagogy rooted in distinctive human societies. However, indigenous development also limits the school practices to a small repertoire.

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.