Conceptualising Intergenerational Lived Experience: Integrating Art–Moving–Well-Being across Disciplines,Communities and Cultures

Art and movement are motivating forces in, through, and beyond education. As populations age, there is an increasing need to support physical and social well-being. Yet, since the onset of the COVID-19 pandemic, there has been a reported exponential increase in feelings of loneliness across generations. Complex challenges require trans-disciplinary solutions, and this paper represents a joint effort within and across disciplines, communities and cultures to find ways to ameliorate this silent epidemic. In this paper, we propose a cross-disciplinary conceptual framework where Aboriginal Artists and Knowledge Holders, Teacher Educators, and Physical and Occupational Therapists come together to explore theoretical and pedagogical insights that encompass intergenerational art–moving–well-being practices, reducing feelings of loneliness and improving social connections across generations. There are two main aims of this paper; first, to better understand current studies that report on integrating art–moving–well-being practices, and the effect this has on health and well-being of intergenerational participants (under 10-year-olds, 20+ year olds and 50+ year olds). Second, based on community needs, the long-term aim is to propose a flexible art–moving–well-being conceptual model that is scalable, sustainable and based on social and relational support systems. We propose a model that is flexible and adaptable within and across our local community and beyond. We argue that feelings of loneliness are unique to each individual, and there is a need to connect specific intergenerational programmes with art–moving–well-being practices that readily engage and integrate varied communities and cultures in sustainable ways and thus, contribute to thriving communities.     

Narrative research in climate change adaptation—Exploring a complementary paradigm for research and governance

Climate adaptation research increasingly focuses on the socio-cultural dimensions of change. In this context, narrative research is often seen as a qualitative social science method used to frame adaptation communication. However, this perspective neglects an important insight provided by narrative theory as applied in the cognitive sciences and other practical fields: human cognition is organized around specific narrative structures. In adaptation, this means that how we ‘story’ the environment determines how we understand and practice adaptation, how risks are defined, who is authorized as actors in the change debate, and the range of policy options considered. Furthermore, relating an experience through story-telling is already doing ‘knowledge work’, or learning. In taking narrative beyond its use as an extractive social research methodology, we argue that narrative research offers an innovative, holistic approach to a better understanding of socio-ecological systems and the improved, participatory design of local adaptation policies. Beyond producing data on local knowledge(s) and socio-cultural and affective-emotive factors influencing adaptive capacity, it can significantly inform public engagement, deliberation and learning strategies–features of systemic adaptive governance. We critically discuss narrative as both a self-reflective methodology and as a paradigmatic shift in future adaptation research and practice. We explore the narrative approach as a basis for participatory learning in the governance of socio-ecological systems. Finally, we assemble arguments for investing in alternative governance approaches consistent with a shift to a ‘narrative paradigm’.     

When do firms rely on their knowledge spillover recipients for guidance in exploring unfamiliar knowledge?

Knowledge spillover occurs when recipient firms combine the knowledge of an originating firm with other knowledge. When recipient firms combine the originating firm's knowledge with knowledge that is unfamiliar to the originating firm, the recipient firms potentially provide insight to the originating firm on the viability of exploring such knowledge. By mimicking its recipient firms, the originating firm reduces the challenge and uncertainty of exploring unfamiliar knowledge domains. We examine the exploration activities of 87 telecommunications equipment manufacturers over a ten-year time period. We argue that those firms that operate in competitive and dynamic market environments promoting conservative risk-taking behavior will value such uncertainty reduction more highly and thus rely to a greater extent on their recipient firms for guidance on where to explore for new expertise. In contrast, firms in high-growth market environments are more likely to look beyond the activities of recipient firms when exploring new technological domains and rely less on mimicking their recipient firms.     

Work-related factors influencing doctors search behaviors and trust toward medical information resources

Regulation of clinical practice is a characteristic aspect of the medical profession. Regardless of whether this regulation derives from government-sourced guidelines or materials from government-sponsored institutions, it results in a high production of information resources (institutional information resources), which are disseminated to the clinical stuff in order to ensure compliance. In that case, the issue of credibility of these information resources might arise, since medical practice is characterized by a high frequency of change. The latter involves a continuous effort on the part of the clinical staff, which is motivated by work-related factors (e.g., need for compliance) or personal motivation (e.g., need for self-improvement). In this study we consider a simple trust model, according to which we assume that perceived trust is a direct antecedent of perceived credibility. We evaluate whether work-related or personal motivating factors influence the relation between perceived credibility and trust toward institutional information sources and how the effect of each factor affects this relation. Findings suggest that work-related factors have a higher impact on the relation between credibility and trust than personal motivation factors, while they are stressing the important role of hospital libraries as a dissemination point for government-sponsored information resources.     

知识管理自学习案例平行结构体方面相似度模型

为建立知识管理系统的自学习与自组织机制,本文将基于案例推理的技术思想引入知识管理自学习子系统,对知识管理自学习案例的平行结构体方面相似度进行系统而深入地研究。首先,论文对相关文献进行述评,阐明了本文研究的出发点,诠释了知识管理自学习案例平行结构体方面的内涵及其特征;继而,分七种情况深入讨论了平行结构体方面维度相似度的计算方法,并在此基础上提出了方面相似度的计算策略;最后,通过算例分析了相关技术策略与实施细节。     

组织中个人知识是如何生成与创新的——基于知识整体论的视角

组织中个人知识生成与创新是组织获取竞争力的关键。理性论与经验论对立成为传统西方知识论的特色,基于此之上的个人知识创新理论关注隐性知识与显性知识以及二者互相转换形成螺旋上升过程,忽视了由人的情感与自由等驱动的活性知识,同时也忽略了情感在知识转换过程中的地位。从知识整体论角度出发,提出了组织中个人知识的生成与创新路径,以及个人知识互相转换的九大模式,从而揭示了组织中个体知识创新的推动力。     

No inventor is an island: Social connectedness and the geography of knowledge flows in the US

Do informal social ties connecting inventors across distant places promote knowledge flows between them? To measure informal ties, we use a new and direct index of social connectedness of regions based on aggregate Facebook friendships. We use a well-established identification strategy that relies on matching inventor citations with citations from examiners. Moreover, we isolate the specific effect of informal connections, above and beyond formal professional ties (co-inventor networks) and geographic proximity. We identify a significant and robust effect of informal ties on patent citations. Further, we find that the effect of geographic proximity on knowledge flows is entirely explained by informal social ties and professional networks. We also show that the effect of informal social ties on knowledge flows is greater for new entrepreneurs or ‘garage inventors’, for older or ‘forgotten’ patents, and for flows across distant technology fields. It has also become increasingly important over the last two decades.     

Fake news detection via knowledgeable prompt learning

The spread of fake news has become a significant social problem, drawing great concern for fake news detection (FND). Pretrained language models (PLMs), such as BERT and RoBERTa can benefit this task much, leading to state-of-the-art performance. The common paradigm of utilizing these PLMs is fine-tuning, in which a linear classification layer is built upon the well-initialized PLM network, resulting in an FND mode, and then the full model is tuned on a training corpus. Although great successes have been achieved, this paradigm still involves a significant gap between the language model pretraining and target task fine-tuning processes. Fortunately, prompt learning, a new alternative to PLM exploration, can handle the issue naturally, showing the potential for further performance improvements. To this end, we propose knowledgeable prompt learning (KPL) for this task. First, we apply prompt learning to FND, through designing one sophisticated prompt template and the corresponding verbal words carefully for the task. Second, we incorporate external knowledge into the prompt representation, making the representation more expressive to predict the verbal words. Experimental results on two benchmark datasets demonstrate that prompt learning is better than the baseline fine-tuning PLM utilization for FND and can outperform all previous representative methods. Our final knowledgeable model (i.e, KPL) can provide further improvements. In particular, it achieves an average increase of 3.28% in F1 score under low-resource conditions compared with fine-tuning.     

Dynamic commonsense knowledge fused method for Chinese implicit sentiment analysis

Compared with explicit sentiment analysis that attracts considerable attention, implicit sentiment analysis is a more difficult task due to the lack of sentimental words. The abundant information in an external sentimental knowledge base can play a significant complementary and expansion role. In this paper, a sentimental commonsense knowledge graph embedded multi-polarity orthogonal attention model is proposed to learn the implication of the implicit sentiment. We analyzed the effectiveness of different knowledge relations in the ConceptNet knowledge base in detail, and proposed a matching and filtering method to distill useful knowledge tuples for implicit sentiment analysis automatically. By introducing the sentimental information in the knowledge base, the proposed model can extend the semantic of a sentence with an implicit sentiment. Then, a bi-directional long–short term memory model with multi-polarity orthogonal attention is adopted to fuse the distilled sentimental knowledge with the semantic embedding, effectively enriching the representation of sentences. Experiments on the SMP2019-ECISA implicit sentiment dataset show that our model fully utilizes the information of the knowledge base and improves the performance of Chinese implicit sentiment analysis.     

Multi-heterogeneous neighborhood-aware for Knowledge Graphs alignment

Entity alignment is an important task for the Knowledge Graph (KG) completion, which aims to identify the same entities in different KGs. Most of previous works only utilize the relation structures of KGs, but ignore the heterogeneity of relations and attributes of KGs. However, these information can provide more feature information and improve the accuracy of entity alignment. In this paper, we propose a novel Multi-Heterogeneous Neighborhood-Aware model (MHNA) for KGs alignment. MHNA aggregates multi-heterogeneous information of aligned entities, including the entity name, relations, attributes and attribute values. An important contribution is to design a variant attention mechanism, which adds the feature information of relations and attributes to the calculation of attention coefficients. Extensive experiments on three well-known benchmark datasets show that MHNA significantly outperforms 12 state-of-the-art approaches, demonstrating that our approach has good scalability and superiority in both cross-language and monolingual KGs. An ablation study further supports the effectiveness of our variant attention mechanism.