Fudan Journal of the Humanities and Social Sciences最新文献

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.