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.     

The social as the heart of social innovation and social entrepreneurship: An emerging area or an old crossroads?

This paper aims to contribute to the conceptual debate on the connection between social innovation and social entrepreneurship, considering the role of the ‘social’ within this connection. Supported by a systematic literature review (SLR) with an in-depth analysis of 34 articles from Scopus-indexed and Web of Science databases journals, this paper identifies, analyzes and describes the difficulties and opportunities in the social innovation and social entrepreneurship literature. Little is known about the link between both concepts and the influence of the ‘social’ in their development and implementation. This SLR was conducted to identify and describe definitions and patterns. Results show that the connection between social innovation and social entrepreneurship is in its take-off phase, but it still is a fragmented field with a huge lack of consensus. Thus, it will be important to see where the field will head, as this paper aims to be a first step in the understanding of social innovation and social entrepreneurship through a collective and integrated perspective, providing an elucidation of the different perspectives of the literature.     

土地经营对中国城市空间扩张的驱动机制——基于273个地级市的实证分析

分税制改革后,地方政府采取"以地生财、以财养地"的城市土地经营模式,凭借土地出让获取土地收益,并用于城市基础设施等城市建设投资,以推动城市经济发展,但土地经营对城市规模的空间扩张机制仍有待理清.基于地方政府土地收益和城市建设投资两个维度,本文构建了中国地方政府土地经营驱动的城市空间扩张理论逻辑,选择2003-2017年...     

工业企业不同类型绿色技术创新内在关联与绩效传导路径

绿色技术创新可划分为末端治理技术创新、绿色工艺创新和绿色产品创新,在改进环境绩效和市场竞争力的同时,也能够提高企业的经济绩效。本文构建了绿色技术创新升级和经济绩效改善的关系模型,以642家工业企业为样本进行实证分析。研究结果表明：①企业3类绿色技术创新均能够显著改善其环境绩效和市场竞争力。②企业环境绩效和市场竞争力是经济绩效传导过程中的重要中介变量。③企业末端治理技术创新能够促进绿色工艺创新和绿色产品创新,企业绿色工艺创新能够正向影响企业的绿色产品创新,即不同类型绿色技术创新之间存在技术创新升级的空间。④不同类型绿色技术创新向经济绩效传导的路径存在一定的差异性,表现为绿色工艺创新能够直接产生经济绩效,而末端治理技术创新、绿色产品创新只能通过间接路径提高企业的经济绩效。通过上述研究识别了企业3种不同类型绿色技术创新之间的传导关系,对比分析了不同类型绿色技术创新经济绩效提升的异同性,给出了更多的企业绿色技术创新经济绩效提升的路径,对于深化绿色技术创新过程认识及优化政策设计具有重要意义。     

循证决策视角下的患者健康咨询主题分析

[目的/意义]为了将大数据更好地用于循证决策,文章构建了循证决策视角下的患者健康主题分析模型。[方法/过程]首先使用LDA主题模型对高血压问答数据进行主题探测,并结合文献提炼出高血压八大主题;再次使用问卷调查法和层次分析法对主题探测结果进行评估;最后对得到的证据进行实践讨论。[结果/结论]文章对大数据和循证决策的结合做了尝试性探索,结果表明循证决策视角下的患者健康主题分析模型,具有一定的实用性和创新性。     

城市优质品牌资源对经济效率的影响

品牌资源作为最重要的软环境要素之一,对提升城市竞争力与促进高质量发展具有重要意义。本文以世界品牌实验室《中国500最具价值品牌》榜单为基础,以上榜品牌所在城市为研究对象,建立2005—2016年的面板数据,利用双向固定效应模型实证探究了城市优质品牌资源对经济效率的影响。结果显示：①城市优质品牌资源能够促进生产性服务业集聚、提高对外合作水平和创新水平,进而提高城市经济效率,尤其是纯技术效率;②第二产业的优质品牌资源对城市经济效率有促进作用但不显著,第三产业的优质品牌资源能够显著促进城市经济效率提高,而且生产性服务业的优质品牌资源比生活性服务业的优质品牌资源更能促进城市经济效率的提高;③同样的品牌资源规模下,增加品牌数量不能显著提高城市经济效率,而打造优质品牌多样化则有利于城市经济效率提高。本文结果有助于深入了解品牌资源驱动城市经济效率的机制与结果,并为进一步培育优质品牌资源、提高城市经济效率提供借鉴。     

中国金融资源配置效率时空异质性及其影响因素

提升金融资源配置效率是防范重大金融风险以及推动经济高质量发展的重要途径。基于StoNED模型测算了2007—2019年中国31个省份的金融资源配置效率,剖析中国金融资源配置效率时空异质性,并采用时空地理加权回归模型分析了时空异质性的影响因素。结果表明：①2007—2019年,整体上中国金融资源配置效率不高,具有“先上升后下降、再上升而后缓慢下降”的“M”型时序变化特征。②中国金融资源配置效率呈现总体分片、块状聚集的“连片状”空间格局,且东部地区金融资源配置效率明显高于中西部地区。③金融结构、财政支出能有效支持各省份金融资源有效配置;经济水平、金融水平、信贷结构、储蓄规模、金融开放、产业结构高级化和所有制结构对各省份金融资源配置效率的影响作用存在异质性。本文研究发现为新时期如何推进金融资源配置提效、优化金融资源布局与协调区域经济发展提供了决策参考。     

基于CGE模型的海洋生态补偿宏观经济效应

近年来,海洋经济的快速发展导致海洋生态坏境面临的压力越来越大。为保护海洋生态系统,实现海洋自然资源的可持续利用,亟需建立相应的海洋生态补偿机制,并探索生态补偿的标准问题。本文通过编制2017年中国宏观经济社会核算矩阵,构建中国宏观经济可计算一般均衡模型,模拟了征收不同程度的海洋生态补偿税对经济活动的影响。研究发现：①在征收生态补偿税的情形下,将政府收入增加的部分作为海洋生态补偿的专项资金,能够有效提高海洋生态补偿量。在生态补偿税设置为增值税的5%的情况下,海洋生态补偿量较初始情形相比,提升了4.07倍。②生态补偿税的征收会引起居民收入、企业收入、国外储蓄和国内生产总值的下降,对经济发展产生一定的阻碍作用。其中,居民收入和国外储蓄受影响较大,企业收入和国内生产总值受影响较小。因此,需要制定合理的生态补偿标准和政策,实现生态环境和社会经济的协调发展。     

AugPrompt: Knowledgeable augmented-trigger prompt for few-shot event classification

Few-Shot Event Classification (FSEC) aims at assigning event labels to unlabeled sentences when limited annotated samples are available. Existing works mainly focus on using meta-learning to overcome the low-resource problem that still requires abundant held-out classes for model learning and selection. Thus we propose to deal with the low-resource problem by utilizing prompts. Further, existing methods suffer from severe trigger biases that may result in ignorance of the context. That is, the correct classifications are gained by looking at only the triggers, which hurts the model’s generalization ability. Thus, we propose a knowledgeable augmented-trigger prompt FSEC framework (AugPrompt), which can overcome the bias issues and alleviates the classification bottleneck brought by insufficient data. In detail, we first design an External Knowledge Injection (EKI) module to incorporate an external knowledge base (Related Words) for trigger augmentation. Then, we propose an Event Prompt Generation (EPG) module to generate appropriate discrete prompts for initializing the continuous prompts. After that, we propose an Event Prompt Tuning (EPT) module to automatically search prompts in the continuous space for FSEC and finally predict the corresponding event types of the inputs. We conduct extensive experiments on two public English datasets for FSEC, i.e., FewEvent and RAMS. The experimental results show the superiority of our proposal over the competitive baselines, where the maximum accuracy increase compared to the strongest baseline reaches 10.8%.     

基于因子分析与交叉DEA的国家自主创新效率评价——以山东半岛国家自主创新示范区为例

从科技创新投入与产出效率入手,构建国家自主创新示范区创新能力评价指标体系,将各指标按照投入和产出的性质进行分类,并用因子分析方法对其进行处理,最后利用交叉DEA模型评价山东半岛国家自主创新示范区中的6个高新技术开发区的科技创新效率,研究得出山东半岛国家自主创新效率是DEA有效,但其交叉效率水平仍处于中等水平,创新效率需进一步提高。