In recent years, the development of cultural and creative products by publishing institutions has become an important means to connect readers, drive transformation, and create added value. Within the overall landscape of publishing-related cultural and creative products, the development of products derived from classic literary works offers distinctive value but also presents unique challenges. The key to its success lies in constructing an "open system of meaning,
Against the background of pressure in the book-retail market, intensified competition for platform traffic, and the growth of content commerce, influencer marketing helps publishers generate incremental demand, reach segmented readers, and circulate books across online communities. Yet changing traffic mechanisms, dispersed influencer resources, and rising matching and cooperation costs have exposed the limits of traditional practices based on experience, broad outreach, and opportunistic collaboration. Based on the resource action perspective, this study examines how artificial intelligence is being incorporated into influencer marketing in publishing and how it may change the identification, combination, and use of book content, influencer resources, platform traffic, and reader demand. The study used semi-structured interviews with 14 practitioners from 14 publishing organizations. Two researchers coded the interview materials to examine current application patterns, the forces driving change, and possible directions of development. Three application areas were identified: influencer screening and matching; content-resource provision and asset accumulation; and business operations and process coordination. Specific practices include integrating influencer data to support recommendations, decomposing books into customized promotional materials and reusable content assets, and assisting cooperation follow-up and information sharing. These applications reduce information-processing and communication costs, but they remain constrained by unstable outputs, dependence on prompting, and hallucinations. At present, artificial intelligence mainly improves local workflows rather than fully replacing extensive outreach or temporary resource bricolage. Further analysis attributes the emerging shift to changes in the external resource environment, growing complexity in internal marketing tasks, and the enabling conditions created by artificial intelligence. Publishers therefore need to reorganize the resource-action chain in four respects. Resource identification should move beyond follower counts and historical sales by combining book-content features, influencer performance, user feedback, and network relationships. Decision-making should incorporate auxiliary attribution, counterfactuals and process monitoring, with human correction used to improve reliability and interpretability. Influencer resources should be called upon in tiers, with portfolios and campaign pacing adjusted to pre-launch promotion, content interpretation, concentrated conversion, and long-tail diffusion. Marketing relationships should also become layered, combining sustained cooperation with key influencers and flexible participation by peripheral creators. The study conceptualizes influencers as relational resources that connect reader demand, translate the value of books, and help publishers absorb market and user knowledge. Artificial intelligence can strengthen publishers' capacity to identify, evaluate, use, and coordinate such resources, creating conditions for a shift from temporary resource assembly to more systematic resource action. This potential, however, depends on reliable outputs, human oversight, accumulated organizational data, and continuing coordination with influencers. The analysis provides a resource-action explanation of artificial intelligence's role in publishing marketing and offers practical guidance for influencer-resource management, humanu2013AI collaboration, and more efficient resource use. Publishers must convert isolated tools into sustained, interpretable, and reliable decision support and relationship management.
Problem awareness is widely recognized as a core driving force of research in the humanities and social sciences. However, in editorial and publishing practice, editors and authors often fail to reach a consensus on "what constitutes a genuine problem" due to differences in disciplinary backgrounds and cognitive frameworks. This paper argues that transforming problem awareness from an individual cognitive trait into a shared methodological framework between editors and authors is a possible pathway to address this issue. This study attempts to demonstrate the feasibility of sharing problem awareness through dialogue at three levels: theoretical, historical, and empirical. Specifically, theoretical dialogue assesses the depth of a problem's connection to academic traditions; historical dialogue evaluates the temporal depth of a problem; and empirical dialogue determines whether a problem is rooted in the lived world and authentic experience. Together, these three dimensions form a practical tool that can guide interdisciplinary manuscript review. At the same time, the paper acknowledges the inherent limits of such sharing, including individual cognitive differences, disciplinary barriers, and the unique research paradigms of certain specialized fields. The necessity of problem awareness sharing arises from several practical challenges that confront academic publishing. As interdisciplinary research continues to gain momentum, editors of comprehensive humanities and social science journals frequently find it difficult to evaluate manuscripts that fall outside their own areas of expertise. A heavy reliance on peer review alone often privileges disciplinary technicalities at the expense of the broader public significance of a research question. Meanwhile, authorsu2014especially early-career researchersu2014often equate problem awareness with trending topics, policy relevance, or novel perspectives, rather than with questions that genuinely advance collective knowledge. In this context, problem awareness sharing offers a common language through which editors and authors can jointly identify, assess, and refine research questions. This diagnostic framework also demonstrates its practical value in three key areas of editorial work. First, it enhances manuscript review accuracy by helping editors move beyond superficial criteria and systematically evaluate the theoretical, historical, and empirical contributions of a submission. Second, it improves communication efficiency by replacing vague judgments such as "insufficient problem awareness" with structured, actionable feedback that authors can readily understand and act upon. Third, it strengthens editorial planning by enabling editors to move from being passive recipients of submissions to active co-creators of research agendas with authors. The paper concludes by emphasizing that problem awareness sharing does not aim to impose a fixed standard for evaluating research questions, but rather offers a flexible and debatable reference framework. Striking a balance between the potential for sharing and its inherent limitations reflects both the professional wisdom of editors and the broader imperative of constructing a distinctive knowledge system for the Chinese humanities and social sciences.
Funding policies can regulate the expenditure of article processing charges (APC) for gold open access (gold OA) papers, but cannot coordinate the management of different publishing models. Publishing models can, however, be compared systematically along the dimension of paper impact. Based on the data of Chinese papers included in the Web of Science (WoS) database from 2015 to 2024, this paper adopts Spearman rank correlation and non-parametric Kruskal-Wallis test methods to explore the influence mechanism of publishing model evolution on international collaborative papers. Chinese SCI papers are categorized into four groups based on the two-dimensional framework of u201Ccollaboration type u00D7 publishing modelu201D, namely international collaboration with gold OA, international collaboration with non-OA, domestic collaboration with gold OA, and domestic collaboration with non-OA. A three-year rolling-average smoothing procedure is applied to improve the robustness of the correlation analysis. The empirical results reveal significant differentiated correlation characteristics between collaboration types and the academic influence of Chinese SCI papers. To ease the tension between these factors and paper impact, it is essential to strengthen international collaboration and prioritize the publication of high-potential highly-cited papers in Q1 journals; improving the content quality of domestically collaborative papers can systematically optimize core academic indicators, while citation improvement strategies for non-collaborative papers from single domestic institutions require strict effectiveness verification. Further analysis shows that structural adjustments in international publishing in recent years have raised the acceptance threshold for non-OA papers, leading to the concentration of Chinau2019s high-quality international collaborative resources in the non-OA publishing model, while the gold OA publishing model only serves as a fallback channel for suboptimal international collaborative resources in China. Such adjustments have also driven the expansion of non-collaborative, non-OA papers in China, and have particularly intensified the outflow of domestically collaborative non-OA papers u2014 which deliver higher academic influence than domestic gold OA papers u2014 and restrained the growth of internationally collaborative non-OA papers with high category normalized citation impact (CNCI). Such structural changes have triggered the one-way closed outflow of high-level academic papers through non-OA channels. This not only endangers national high-level scientific and technological information security, but also weakens the internal circulation efficiency of Chinau2019s OA knowledge innovation, further exposing the structural dilemma of u201Chigh output with insufficient high-quality achievementu201D of Chinese SCI papers. Thereforeu2019s indigenous gold OA publishing system urgently needs to be deeply embedded in the global open science ecosystem so as to enhance the systemic capacity of Chinau2019s high-level opening-up. Finally, phased paper backflow strategies are proposed from the perspective of overall management of publishing models: constructing a domestic free diamond OA platform to attract the backflow of domestic collaborative non-OA papers and strengthen the internal circulation efficiency of Chinau2019s OA knowledge innovation; strengthening public oversight of the promotional performance of international platforms so as to build a sound competition mechanism among international publishers in terms of academic impact; and hedging backflow risks through an academic influence compensation mechanism.
Competition in the artificial intelligence industry is increasingly shifting from a contest of general-purpose capabilities to deep industry applications, yet the insufficient supply of high-quality industry datasets has become the key bottleneck constraining the "AI+" empowerment of diverse industries. Science and technology publishers (STPs), with their long-accumulated professional knowledge resources and deep-rooted industry networks, are well positioned to become core suppliers, yet they simultaneously face multiple challenges including lagging strategic awareness, structural supply-demand mismatches, high processing costs and technical difficulties, the absence of shared tools and standards, underdeveloped circulation mechanisms, and a critical shortage of interdisciplinary talent. This paper systematically reviews recent national policies on "AI+" and grounded in the core concept of "model-data resonance", elucidates its positive feedback loop: high-quality industry data train more models with stronger domain knowledge, and the application of these models in turn generates new scenarios and produces fresh data, which then feeds back to further optimize the models. The paper clarifies the definition of industry datasets, which encompass multimodal and multidimensional data forms across three hierarchical layersu2014basic data modalities, specialized professional data forms, and knowledge organization structuresu2014and analyzes the driving factors, role positioning, distinctive advantages, and practical challenges that STPs face in constructing such datasets. Building on this analysis, the paper proposes nine concrete pathways at both strategic and implementation levels. Strategically, these include raising strategic awareness of the data economy, strengthening policy research to align with national directives, clearly defining priority industry directions, and fostering a deeply integrated ecosystem that brings together government, industry, academia, research, and application stakeholders. On the execution front, the pathways consist of maintaining the core traditional publishing business while leveraging it as a foundation, adopting a "three-step method" for data identification, annotation, and iterative validation to optimize the development process, co-developing shared tools and standards in collaboration with industry partners, assembling interdisciplinary professional teams that combine editorial, technical, and domain expertise, and placing special emphasis on constructing industry ontology datasets to ensure semantic interoperability and long-term reusability. The paper contends that 2026 marks a pivotal year for China's AI trajectoryu2014the transition from general-purpose conversational AI to practical industry applications. For science and technology publishers, this represents a strategic window of opportunity. Guided by the principle of "model-data resonance", these publishers should accelerate their transformation from traditional content producers to data service providers, converting their accumulated intellectual capital from professional publishing into a distinctive competitive advantage in the intelligent age, thereby providing robust support for building China's independent knowledge system and enhancing the nation's overall scientific and technological competitiveness.
