The impact of artificial intelligence (AI) on the labor market, based on the Routine-Biased Technological Change paradigm, is widely acknowledged. However, existing job classification methods lack detail and accuracy. To address this limitation, the Chinese-BERT-wwm model was optimized to classify recruitment data from listed companies between 2013 and 2019 into routine and non-routine jobs, achieving a test set accuracy of accuracy of nearly 93%. Additionally, the GLM4 model was used to match job titles and descriptions to the "Chinese Occupational Classification (2022 Edition)" to identify digital occupations and analyze the impact of AI technology on labor demand structure. Empirical results show that higher AI technology levels significantly increase demand for non-routine jobs and reduce demand for routine jobs, with pronounced effects in non-state-owned enterprises, high-tech industries, and manufacturing. Further analysis reveals that the increased demand for non-routine jobs is primarily driven by growth in non-routine cognitive positions. Mechanism analysis shows that AI adoption increases non-routine job demand through productivity effects and the creation of new digital occupations, while reducing routine job demand through substitution effects. It expands the application of large language models in economic text analysis.

In order to guide and promote the trading and circulation of data elements, and promote the healthy development of the data trading market, a new quality productive forces "new quality" analysis framework was constructed, and based on this framework, the new quality characteristics of data elements and market development laws were analyzed in depth. The factors that restrict data trading and smooth circulation were deeply explored. The study showed that the trustworthy mechanism is a key link affecting data trading circulation, and credit management for data trading is the future development direction. This system draws on mature experience in credit supervision in other fields, aiming to guide the behavior of all parties involved in data transactions through credit management systems, and solve the problems of insufficient and irregular development of the data element market. The results indicate that building a credit management system for data transactions can ensure the fairness and transparency of on exchange transactions, guide more data transactions to be conducted on the exchange, reduce the risks caused by information asymmetry in off exchange transactions, enhance trust between trading parties, reduce fraudulent behavior, help promote the trustworthy trading and circulation development of data elements, and provide institutional guarantees for the efficient development of new productive forces.

Talent governance is the cornerstone of local governance modernization. However, some regions excessively prioritize talent competition in reality, exacerbating the issue of governance fragmentation. A single-case study of talent governance practices in S Province was conducted from the theoretical perspectives of holistic governance and performance management to explore how local governments address governance fragmentation through the integration of policy tools and governance mechanisms, thereby responding effectively to the dual logic of talent governance: enhancing external competitiveness and optimizing internal resource allocation. The findings indicate that the talent governance system in S Province achieves structural coordination, functional integration and external collaboration through top-level design, while its implementation is energized by a performance evaluation mechanism centered on goal accountability. Grounded in the case analysis, the practical pathways and intrinsic mechanisms of the talent governance system in S Province are deeply analyzed, and a practical framework for local talent governance under the dual-logic perspective is developed, which provide some reference for advancing the modernization of local talent governance.

The value alignment of large language models is a global issue related to ensuring safe collaboration when enterprises and societies adopt these technologies. Achieving alignment between the behavior of large language models and the value intentions of decision-makers as well as societal norms is identified as the core challenge for ensuring safety and trust. Formal rationality and substantive rationality, two philosophical concepts proposed by Max Weber, were introduced to explore value alignment mechanisms. Four value alignment states in enterprise management were categorized including "high formal rationality-low substantive rationality" as technical drift, "high substantive rationality-low formal rationality" as value prioritization, "low formal rationality-low substantive rationality" as alignment failure, and "high formal rationality-high substantive rationality" as dynamic alignment. Transparency, clarity, and sociality were identified as analytical standards for value alignment. Pathways to achieve value alignment in enterprise management were proposed, including the embodiment of cognitive capability in the "technical drift→dynamic alignment" pathway, the clarification of technical intentionality in the "value prioritization→dynamic alignment" pathway, and the construction of meaning in the "alignment failure→dynamic alignment" pathway. The findings provide theoretical support and practical insights into the value alignment mechanisms of large language models in enterprise management.

In recent years, the problem of equalization supply of local public services has been increasingly concerned by all sectors of society. The report of the Party's 20th National Congress and the decision of the Third Plenary Session of the 20th Central Committee also stressed the need to "enhance the equalization and accessibility of basic public services". How to better straighten out the fiscal management system among multi-level governments, and give full play to the two positivity of "superior government" and "local government" in the supply of public services is particularly important for ensuring the equalization and accessibility of public services. From the perspective of the fiscal management system, the relevant theoretical literature on the equalization supply of local public services in China was sorted out. It find that, firstly, significant regional and urban-rural differences exist in the supply of local public services in China, with the imbalance in the supply of education and healthcare being particularly prominent. Secondly, factors including the fiscal decentralization system, local governments' promotion incentives and the fiscal expenditure structure are major causes for the imbalance in local public service supply. Thirdly, public policies like increasing public expenditures, enhancing transfer payments, promoting household registration reform and encouraging government service procurement are conducive to improving the supply of local public services. Finally, reform measures of the fiscal management system, such as the division of affairs rights and expenditure responsibilities and the optimized design of inter-governmental fiscal relations, have a remarkable impact on the balance and accessibility of local public service supply.

The development of AI large models is reshaping the innovation model driven by technology-push and demand-pull, making the interaction mechanisms between the two more closely integrated. However, existing literature lacks a systematic discussion on the innovation process driven by the interaction between demand and technology under the influence of AI large models. For this reason, a case study of AI large model-empowered innovation in the Tmall Genie product was conducted, based on the perspectives of the technology track and market track. The pathways for technology-push, demand-pull, and dual-track interactive innovation enabled by AI large models were extracted. The findings indicate that traditional AI technologies contribute to technology-push innovation by participating in stages such as technology identification, market validation, and testing, while also embedding in demand-pull innovation through stages like user need acquisition, evaluation, and transformation, facilitating the discovery and realization of personalized demands. AI large models enable the synergistic evolution of technology and demand, and support industry upgrading by promoting innovation ideation, technological advancement, bidirectional interaction, iterative innovation, knowledge expansion, and transformation. Compared with the innovation diffusion under the weak coupling mode between technology and demand driven by traditional AI, AI large models, with their significant advantages in expanding "user attributes" "innovator roles" and "knowledge domains" promote innovation diffusion under the strong coupling mode between technology and demand. It provides theoretical foundations and practical insights for enterprise innovation management and industrial upgrading empowered by AI large models.

The incubator has played a significant role in driving the formation of local entrepreneurial ecosystems, revitalizing regional advantages, and establishing sustainable development models with regional characteristics. By focusing on Hongtai Zhizao, a case study was conducted to analyze how the incubator facilitates the evolution of the entrepreneurial ecosystem centered around it. The dynamic coupling and interaction between ambidextrous capacity in the development process of the incubator was identified. It is found that the evolution of the entrepreneurial ecosystem involves three stages. The specific mechanisms through which structural, environmental, and leadership ambidextrous capacity influence the progression of the entrepreneurial ecosystem were examined. From a dynamic perspective, the typical configurations of these three ambidextrous capacity are summarized, clarifying their interactive coupling relationships and the bidirectional interaction between external environments and internal structures. The findings contribute to understanding how incubators drive the evolution of entrepreneurial ecosystems and enrich the research on the coupling architecture of ambidextrous capacity.

Under the "manufacturing power" strategy, enterprise innovation, particularly design innovation, plays a crucial role in transforming China from a manufacturing powerhouse to an innovation-driven economy. However, enterprise design innovation is characterized by a short research and development cycle, quick results, low investment, and minimal risk. Enterprises also exhibit a tendency towards short-term profit-seeking in their design innovation practices, often neglecting long-term objectives. Although research in this area is emerging, a systematic literature review is still lacking. First co-citation analysis theory was used to screen and 518 articles published from 1990 to 2023 based on the subject search terms "enterprise design innovation" and "enterprise innovation design" in CNKI (China National Knowledge Infrastructure) were reviewed. Knowledge mapping and visual analysis techniques were applied to construct visual maps and the progress, hot topics, and future trends in enterprise innovation design research was analyzed. Secondly, the research on the paths of enterprise design innovation driving business development, technological innovation paths, paths of autonomous and collaborative innovation, and their underlying mechanisms were systematically summarized, as well as the driving mechanisms and practical paths. Furthermore, the challenges faced by enterprise design innovation in China were critically discussed, and research gaps and issues were identified. Finally, placeing enterprise design innovation in the era of technological convergence and cross-disciplinary integration, future research directions was proposed. Future research on enterprise design innovation in China should focus on interdisciplinary, cross-field, and cross-regional collaborative studies, comparative research from a global perspective, and integrated studies combining macro and micro-level analyses. It clarifies the growth trajectory and development direction of enterprise design innovation in China and provides valuable references for related research based on China s innovation practices in manufacturing.

The significance, challenges and opportunities of driving innovative development in manufacturing industry with artificial intelligence technologies including large language models were explored. The technological system of large language models was analyzed, its basic engineering concepts were clarified, and the pan-${\mathrm{L}}_{\mathrm{C}}$ theory-a scientific explanation for next token prediction-was presented. Based on the theory, the causes and consequences of some weird behaviors of large language models were explained, giving a more comprehensive and in-depth understanding of large language models. On the basis, three main requirements for artificial intelligence technologies in manufacturing industry were sorted out, and the core difficulties in the integration of large language models and the artificial intelligence brute-force technology were revealed. A closedness-based solution is proposed for the construction of artificial intelligence systems in manufacturing sectors, such that these systems satisfy the main requirements of specialization, logical validity and knowledge ability, as well as explainability and controllability. Finally, the trend of shifting from "industrial application of new technologies" to "sector innovation driven by new technologies" in the high-quality development of manufacturing industry is discussed briefly.

New energy vehicle industry is an important strategic emerging industry for cultivating national new quality productive forces. Based on patent application data from 2013 to 2022 of the three enterprises (BYD, GEELY and WULING), Lotka-Volterra population competition model was constructed to explore out their coopetition relation mode and evolving trends. Main conclusions are as follows. Firstly, coopetition relation has been an important driving factor to stimulate technological innovation evolution. Secondly, their coopetition model shows great heterogeneity among the three leading enterprises. At current stage, "BYD + GEELY” “WULING + GEELY" show such mutually promoting type. Meanwhile, " BYD +WULING" show the competition-cooperation type. Thirdly, in accordance with simulation results, innovation outputs of enterprises vary with coopetition relation coefficient changes. Innovation output effect of new vehicle industry is positive when the coopetition intensity of mutually-prompting type grows. The positive innovation effect from "WULING + GEELY" coopetition intensity growth is more than that from the other one. Therefore, cultivating orderly competition and coordinated cooperation mode mechanism, especially most potential competition types, should be paid more focus on realizing its high-quality innovation outputs and supporting its new-quality productivity formation for the new energy vehicle industry of China.