Digital Capitalism, as the embodiment of capital logic in data space, gives rise to persistent structural tensions in its rights architecture. Under algorithmic governance, rights-bearing individuals are reduced to data points, while the meaning of rights is reconfigured into computable variables under platform monopolies. The realization of rights is simultaneously constrained by the expansionary demand of data capital and the domination exercised through computing power. This produces a dual alienation: the recognition of rights is obscured by the myth of technological determinism, and the protection of rights is weakened by capital-driven algorithmic design. The root cause lies in the reconstruction of traditional rights relations by the data value chain, subordinating rights claims to the accumulation logic of digital capital. To address this paradox, a two-way corrective mechanism is needed: empowering digital labor through data democratization and fair distribution, while constraining capital power through algorithmic regulation and auditing. Such institutional change is essential not only for safeguarding rights in the digital era, but also for advancing social fairness and justice.
Faced with the deepening trends of population aging, the increasing proportion of the oldest-old, and the challenges of persistently low fertility intentions and low birth rates, establishing a comprehensive population service system that covers all demographic groups and the entire life cycle, with a focus on the "Old and Young", constitutes a systematic social governance initiative for Beijing in the new era to actively address the issues of an aging population and sub-replacement fertility. However, influenced primarily by the weakening care capacity of families as the main caregivers, the mismatch between social service supply and demand, and insufficient precision in safeguarding key groups, the development of "Old and Young" initiatives in Beijing still faces severe challenges. Accordingly, this paper proposes to ground the approach in the specific context of Beijing's "Old and Young" situation, leverage the advantages of a megacity's scale and institutional resources, and focus on building a new framework for the "Old and Young" initiatives characterized by "Quality Home-Caring and Shared Social Responsibility", which integrates traditional Chinese cultural elements with modern governance effictiveness. Specifically, it suggests adopting a life-cycle human capital investment perspective to coordinate the governance of aging, utilizing "Developmental Family Policies" to comprehensively empower families in their primary caregiving roles for the "Old and Young", and implementing targeted measures to provide differentiated care for key groups among the "Old and Young".
From a metaphilosophical perspective, the entanglement between philosophy and history is deeper, broader and more philosophically significant than that is commonly understood. This entanglement serves as both the starting point of philosophical self-reflection and a unique pathway to historical understanding. First of all, the inherent historicity of "philosophy" as a concept of family resemblance is manifested in the contextuality of philosophy and the meta-theoretical value of the history of philosophy. Secondly, the history of philosophy has the characteristics of disciplinary independence, autonomy and non-reducibility. At the same time, it is interdependent with philosophy: the division between the "inner history" and the "outer history" of philosophy reveals the dual functions of historiography—it not only provides an anchor point for historical reflection of philosophy, but also activates the participation of contemporary thought through the study of the history of historiography. Finally, the entanglement between the history of philosophy and the philosophy of history is manifested as a two-way support: the history of philosophy provides a historical resource library for the reconstruction of contemporary theories, while the philosophy of history injects methodological consciousness into the historiography of philosophy. The tension between the two constitutes the dynamic mechanism of philosophical innovation.
This paper challenges the claim that "correlation replaces causation" in the big data era, by examining the epistemological and methodological characteristics of big data analysis. Firstly, this article affirms that the correlations generated by big data pose a serious challenge to causality. Secondly, by analyzing the logical differences between correlation and causation, the analysis underscores causation's unique role in providing explanatory direction, eliminating spurious links, and guiding policy, noting that correlation, although having predictive effectiveness, lacks causal inference's power for counterfactual reasoning and intervention. Finally, using data-intensive science as an example, it shows how big data and causal reasoning can merge epistemologically, methodologically, and ontologically——through tools like Bayesian updating and intervention detection, highly isomorphic with the causal inference framework, which treat causation as a data-approximated mechanism. This article contends that we should transcend the binary opposition between correlation and causation and promote their complementarity at the methodological level.
Algorithmic discrimination arises from the coupling of multiple logics. The historical bias and sample bias of data layer are its "source input", the operation mechanism, design logic, training mode and technical characteristics of algorithm layer constitute "internal self-cause", the technical system and organizational structure of system layer lead to "environmental catalysis", and the power structure and cultural bias of social layer provide "root support". Algorithmic discrimination gives rise to multiple ethical dilemmas, in terms of fairness and justice, formal equality diverges from substantive justice; in terms of responsibility attribution, both the respensible subjects and the relocation of responsibility remains unclear; in terms of autonomy, algorithm agent oversteps human subject; and in terms of human dignity, instrumental rationality devalues the human subject. The governance of algorithmic discrimination needs to build a collaborative framework of technology-law-ethics-society, build the technical foundation of algorithmic fairness through technical correction, build the institutional constraints of algorithmic discrimination through legal regulation, shape the value consensus of algorithmic application through ethical guidance, and unite the governance of algorithmic goodness through social co-governance, so as to realize algorithmic fairness and justice.
The oscillation between individual service and social change has been a defining characteristic of social work over the past century. This feature has not only driven the continuous development of the profession but also resulted in a persistent dualism between theory and practice concerning the relationship between the individual and society. The Theory of Social Mutual-Construction transcends this dichotomy by offering an integrative framework that emphasizes the mutual shaping and co-evolution of individual-society relations. At the individual level, modern persons embody the "three-in-one" character, representing the concrete unity of multiple attributes. At the societal level, the interplay between natural and human systems constructs a social context with dual attributes.Thus, individuals and society are not only interdependent entities but also active agents that continually shape one another, giving rise to the "mutual-construction domain" of their relationship. All objects of social work practice are situated within this domain and its processes of interactive construction, where the concordant evolution, conflicting tensions, and reciprocal formation of individual-society relations are manifested. Accordingly, the Theory of Social Mutual-Construction provides a solid theoretical foundation for bridging the long-tandings divide between theory and practice, particularly the gap between the micro-level of the individual and the macro-level of society.
In the context of comprehensive rural revitalization, returning migrant workers face structural contradictions and upgrading pressures. Under dual survival-development stress, some move from initial ventures to sustained entrepreneurship, making continuous, innovative adjustments in markets, technology, and organization to achieve sustained entrepreneurship. Based on action-strategy analysis of garment entrepreneurs in Town Z, County X of central Chinese province, the study identifies four traits: inevitability, conformity, transformation, and agency. Under the combined influence of structural shocks from industrial upgrading and technological iteration as well as institutional constraints arising from rising labor costs, narrowing secondary markets, and weakening rural organization, entrepreneurship dominated by first-generation returnee migrant workers is shifting from passive response to proactive transformation. These factors also constitute practical challenges. The study recommends building a robust entrepreneurial policy support network to enable coordinated action by government, social organizations, and market actors.
Enterprises are both producers and processors of data. With the diversified practice of corporate financing, enterprise data has become a new object of non-monetary capital contribution in the digital economy era. However, the non-physical nature, complex ownership, and value volatility of enterprise data make the risk of capital inadequacy more prominent compared to traditional contribution assets such as currency and physical assets. To prevent the dilution of company capital, enterprise data as a contribution asset must simultaneously meet three special requirements: "independent transferability", "value stability", and "limited exclusivity". "Value stability" requires that enterprise data be a separate object of property rights contribution and not be attached to other tangible or intangible objects such as property rights or intellectual property rights. "Independent transferability" requires that the contributing entity enjoys the most complete rights over the enterprise data, including the right to hold, use, dispose (centered on operation), and receive income. Under the requirement of "limited exclusivity", although data formed from an individual's own information cannot be directly contributed, the data processor can be restricted from contributing the data, thereby constituting a restriction on the right to contribute; enterprise data does not have the priority effect to exclude individuals from exercising their rights over their personal information in accordance with the law.
Disclosing data asset information can help enterprises establish a good image and enhance their value. Using data on China's A-share listed firms from 2011 to 2022, this essay explores the employment effect of corporate data asset disclosure both theoretically and empirically. Empirical results show that the disclosure of enterprise data asset information can significantly expand the employment scale of enterprises, and this conclusion has passed multiple robustness tests. Mechanism tests have found that the disclosure of enterprise data asset information has reduced the degree of financing constraints for enterprises and enhanced their technological innovation levels, thereby expanding the employment scale of enterprises. Further analysis reveals that the disclosure of enterprise data asset information has a significant positive impact on the employment scale of technology-intensive enterprises. Such disclosure can significantly expand the employment scale of highly educated and highly skilled employees. Therefore, efforts should be made to further promote the disclosure of enterprise data asset information, formulate differentiated data assetization paths for different types of enterprises, and provide corresponding policy support for enhancing the employment creation capacity of enterprises.
The rapid advancement of the new technological revolution, particularly Artificial Intelligence (AI), is profoundly reshaping the operation of civil justice. AI demonstrates high efficiency and accuracy in case allocation, legal research, precedent comparison, and judicial assistance, contributing to improved judicial efficiency, reduced labor costs, and enhanced consistency in rulings. However, its application also faces complex challenges, including ethical judgment, data bias, privacy protection, and legal applicability. Without clear boundaries and supervisory mechanisms, AI may undermine judicial independence and fairness. As a judicial tool, AI must remain strictly under the control of judges, ensuring the independence, transparency, and legality of judicial decisions, while establishing clear responsibility allocation and traceable oversight mechanisms to review system design, algorithm optimization and data selection, preventing bias and misuse. Interdisciplinary collaboration plays a crucial role in this process, as the integration of legal and technical expertise not only provides legal and ethical safeguards but also continuously optimizes AI applications through dynamic feedback mechanisms, ensuring alignment with social fairness and the rule of law in diverse judicial practices. By clarifying technical boundaries, ethical requirements, and regulatory pathways, AI can be positioned as a supportive tool within civil justice and foster effective integration between technology and judicial institutions, providing both theoretical guidance and institutional reference.
The deep integration of GAI and education is driving the ontological reconstruction of students' learning time. To understand the restructuring of learning time under technological empowerment, it is essential to clarify the core components and interactive structures of learning time, as well as grasp the original forms of sovereignty, rhythm, and value in traditional education. Furthermore, the practical representation of GAI's reconstruction of learning time is systematically elucidated from four dimensions: reducing inefficiency in time allocation, de-solidifying time forms, individualizing time rhythms, and diversifying time values. This reconstruction is a non-linear optimization, accompanied by the risk of technological alienation, manifested as algorithm hijacking caused by the transfer of time sovereignty, digital cages formed by the fragmentation of virtual and real scenes, racing games driven by accelerated pace, and data survival caused by value oriented instrumentalization. To avoid these risks, it is necessory to: establish a human-machine collaborative decision-making mechanism to safeguard time sovereignty; promote embodied virtual-real integration to bridge the digital divide; develop a coordinated regulatory system combining technology and humanities to resist accelerated alienation; and reshape growth-oriented time ethics to counter the instrumentalization shift.
