Fudan Journal of the Humanities and Social Sciences最新文献

Bonded labor, also known as debt slavery, has emerged as the predominant form of debt bondage in numerous developing economies. In the context of Pakistan, despite its legal prohibition, the practice is supported by an influential segment of society. This phenomenon is deeply rooted in a matrix of governmental failure, autocratic governance structures, pervasive social inequalities, and the systemic marginalization of specific communities. This study uses a distinctive dataset to scrutinize the statistical dynamics of bonded labor in Pakistan. Utilizing the Cox proportional hazards model, we investigate the likelihood of individuals resorting to debt bondage in Pakistan. Our analysis identifies a constellation of critical factors intimately linked with the profiles and household backgrounds of individuals entangled in debt bondage. These determinants encompass inherited familial wealth, acute health crises, unforeseeable catastrophic occurrences, household income levels, educational attainment, the burden of dependents, and the financial implications of dowry obligations. Furthermore, our research elucidates a significant association between the economic marginalization within labor markets and the wage disparities underscored and exacerbated by debt bondage contracts.

This paper explores the plausible approach of interreligious dialogue in a secular world. It first examines Taylor’s account of the immanent frame in his A Secular Age. This helps us to grasp the moral spiritual outlook of the modern world and the underlying moral concerns of the controversies between the religions and secularists. I then examine Taylor’s claim regarding the indispensability of transcendence in achieving the fullness of human life which is criticized by non-transcendentists and naturalist mundane transcendentists. I argue that the phenomenon of these controversies, on the one hand, is consistent with Taylor’s account of the Nova effect in the secular world, and, on the other hand, that assessing these different versions of transcendence via Taylor’s historical hermeneutical approach or Wainwright’s inference to the best explanation may raise the problem of Christian-centric or epistemic circularity. Furthermore, as we are now living in the immanent frame, interreligious dialogue cannot be implemented without the practical concerns of ordinary life. Inspired by Ricoeur’s idea of testimony and narrative identity, I argue for a kind of interreligious testimonial dialogue which integrates morality, actions, thought and experience into communication, so that it can enhance mutual sympathetic understanding, broaden the life vision between participants, no matter whether religious or nonreligious, and break through the limitation of epistemic circularity.

Most existing studies on the relationship between factor analysis (FA) and principal component analysis (PCA) focus on approximating the common factors by the first few components via the closeness between their loadings. Based on a setup in Bentler and de Leeuw (Psychometrika 76:461–470, 2011), this study examines the relationship between FA loadings and PCA loadings when specificities are treated as latent factors. In particular, we will examine the closeness between the two types of loadings when the number of observed variables (p) increases. Parallel to the development in Schneeweiss (Multivar Behav Res 32:375–401, 1997), an average squared canonical correlation (ASCC) is used as the criterion for measuring the closeness. We show that the ASCC can be partitioned into two parts, the first of which is a function of FA loadings and the inverse correlation matrix, and the second of which is a function of unique variances and the inverse correlation matrix of the observed variables. We examine the behavior of these two parts as p approaches infinity. The study gives a different perspective on the relationship between PCA and FA, and the results add additional insights on the selection of the two types of methods in the analysis of high dimensional data.

Drawing upon Roman public law and the classical Western ius commune generally, I sketch a law-governed constitution of hierarchy, including its institutional form and its basic justification. Grounded in a popular delegation of sovereign authority and power (imperium and potestas) to the Roman emperors and subordinate officials, the constitution of hierarchy is pervasively shaped and constrained by law and legal norms, written and unwritten, that orient the lawful exercise of power to the public good; it includes subsidiary democratic mechanisms of petitioning, consultation, and local and provincial democracy. The alternative to the constitution of hierarchy is not political egalitarianism, but an alternative hierarchy of arbitrary and exploitative rule, dominated by an economic and social class of optimates.

Factor analysis is widely utilized to identify latent factors underlying the observed variables. This paper presents a comprehensive comparative study of two widely used methods for determining the optimal number of factors in factor analysis, the K1 rule, and parallel analysis, along with a more recently developed method, the bass-ackward method. We provide an in-depth exploration of these techniques, discussing their historical development, advantages, and limitations. Using a series of Monte Carlo simulations, we assess the efficacy of these methods in accurately determining the appropriate number of factors. Specifically, we examine two cessation criteria within the bass-ackward framework: BA-maxLoading and BA-cutoff. Our findings offer nuanced insights into the performance of these methods under various conditions, illuminating their respective advantages and potential pitfalls. To enhance accessibility, we create an online visualization tool tailored to the factor structures generated by the bass-ackward method. This research enriches the understanding of factor analysis methodology, assists researchers in method selection, and facilitates comprehensive interpretation of latent factor structures.

In recent years, the development of machine learning has introduced new analytical methods to theoretical research, one of which is Bayesian network—a probabilistic graphical model well-suited for modelling complex non-deterministic systems. A recent study has revealed that the order in which variables are read from data can impact the structure of a Bayesian network (Kitson and Constantinou in The impact of variable ordering on Bayesian Network Structure Learning, 2022. arXiv preprint arXiv:2206.08952). However, in empirical studies, the variable order in a dataset is often arbitrary, leading to unreliable results. To address this issue, this study proposed a hybrid method that combined theory-driven and data-driven approaches to mitigate the impact of variable ordering on the learning of Bayesian network structures. The proposed method was illustrated using an empirical study predicting depression and aggressive behavior in high school students. The results demonstrated that the obtained Bayesian network structure is robust to variable orders and theoretically interpretable. The commonalities and specificities in the network structure of depression and aggressive behavior are both in line with theorical expectations, providing empirical evidence for the validity of the hybrid method.

A simple approach is proposed to study the main factors related to the mining activity’s impact on society, through a corporate social responsibility (CSR) qualitative analysis based on the type of raw materials extracted, either by mine site or firm. A CSR index is defined by 30 environmental and socioeconomic elements and, subsequently, it is weighted by three primary factors; the recycling rate, the transition to green energy, and geographical conditions. The proposed method is adaptable to any change in raw material needs over time and, depending on the analyzed country or region, is applicable to any type of mineral resource. The system can be used to drive engagement with the different stakeholders, add value to a project, and establish a CSR continuous improvement system.

This study empirically examines the nexus between natural resource rent and financial development in the context of the developing economy of Nigeria, between 1990 and 2021, by considering the important role of corruption control under an asymmetric approach. The study further looked at the influence of information technology, and renewable energy, on financial development. The bound test result confirms the existence of a long-term relationship among the variables. This study first uses the nonlinear autoregressive distributive lag (NARDL) model to capture the asymmetry that arises from positive or negative components of natural resource rent. The empirical evidence of the NARDL estimation shows that natural resource rent negatively influences financial development; meanwhile, corruption control boosts financial development and positively moderates this relationship in the Nigerian context. This confirms the existence of a natural resource curse. The results further explained that both information technology, renewable energy, and corruption control enhance financial development. Furthermore, the causality test discovers that there exists a bidirectional causal relationship between financial development and the scrutinized variables. These findings offer valuable policy recommendations for policymakers.

The philosophical debate between “radicalism” and “conservatism” in emerging human enhancement technologies (HET) has never ceased. Although their theoretical claims seem to be distinct from each other, they actually share certain commonalities, which are mainly manifested in three aspects: first, they both hold certain illusions about emerging HET; second, they both reflect the loss of human subjectivity in the debate; and third, they both leave the premise of their propositions in suspense. Based on the reflection and grasp of these commonalities, we can grasp the possible boundaries of the dissolution of the two oppositions: let HET be “where it is in,” reconfigure anthropocentrism, suspend metaphysical myths, and turn to the “reality of human beings.”

Regression estimates are biased when potential confounders are omitted or when there are other similar risks to validity. The instrumental variable (IV) method can be used instead to obtain less biased estimates or to strengthen causal inferences. One key assumption critical to the validity of the IV method is the exclusion assumption, which requires instruments to be correlated with the outcome variable only through endogenous predictors. The chi-square test of model fit is widely used as a diagnostic test for this assumption. Previous simulation studies assessed the power of this diagnostic test only in situations with strong violations of the exclusion assumption. However, low to moderate levels of assumption violation are not uncommon in reality, especially when the exclusion assumption is violated indirectly. In this study, we showed through Monte Carlo simulations that the chi-square model fit test suffered from a severe lack of power (< 30%) to detect violations of the exclusion assumption when the level of violation was of typical size, and the IV causal inferences were severely inaccurate and misleading in this case. We thus advise using the IV method with caution unless there is a chance for thorough assumption diagnostics, like in meta-analyses or experiments.