国内外医学期刊网站现状及分析

通过互联网了解国内外不同医学期刊网站建设的现状,对比发现国内与国外医学期刊的网站建设差距明显.对差距产生的原因做一分析并提出改进建议.     

Delineating policy mixes: Contrasting top-down and bottom-up approaches to the case of energy-storage policy in California

In the light of pressing societal challenges such as climate change and resource scarcity, scholars are increasingly interested in studying policy mixes in the context of sustainability transitions. However, despite numerous conceptual advances and empirical insights, researchers still lack universal criteria or accepted heuristics for delineating policy mixes in these complex policy spaces. We address this gap by conducting an extensive review of the literature, synthesizing best practices, and developing an analytical framework that provides researchers with two archetypical methodological approaches. The top-down approach builds on the idea that the elements of a policy mix originate from an overarching strategic intent. By contrast, the bottom-up approach starts from the definition of a focal impact domain that is affected by a range of policy instruments. For each approach, we outline a systematic analytical procedure, then implement it to scrutinize how policy affects the emerging technological domain of energy storage in California. We find that each approach has particular advantages that render it useful for certain policy mix analyses. Discussing how researchers may choose between the two approaches or leverage their complementarities, we seek to provide the basis for a consistent research program building on the policy mix framework.     

Can we trace back hotel online reviews’ characteristics using gamification features?

Gamification is here to stay, and tourism and hospitality online review platforms are taking advantage of it to attract travelers and motivate them to contribute to their websites. Yet, literature in tourism is scarce in studying how effectively is users’ behavior changing through gamification features. This research aims at filling such gap through a data-driven approach based on a large volume of online reviews (a total of 67,685) collected from TripAdvisor between 2016 and 2017. Four artificial neural networks were trained to model title and review’s word length, and title and review’s sentiment score, using as input 12 gamification features used in TripAdvisor including points and badges. After validating the accuracy of the model for extracting knowledge, the data-based sensitivity analysis was applied to understand how each of the 12 features contributed to explaining review length and its sentiment score. Three badge features were considered the most relevant ones, including the total number of badges, the passport badges, and the explorer badges, providing evidence of a relation between gamification features and traveler’s behavior when writing reviews.     

How to facilitate knowledge diffusion in complex networks: The roles of network structure,knowledge role distribution and selection rule

The diffusion of knowledge within organizations provides opportunities for interpersonal co-operation, improves creative ability and therefore leads to competitive advantage. Focus of prior literature on knowledge diffusion has been on identifying factors that influence individuals' behavioral intentions to seek and share knowledge. However, knowledge diffusion as an enigmatic, emergent and organizational-level process is more than the simple aggregation of individual attributes and needs to be further investigated. Accordingly, this study focuses on three distinct system-level factors, i.e., architectures of connections among individuals, distributions of knowledge roles and designs of selection mechanisms and analyses their effects on knowledge diffusion. To be more specific, we examine three distinct knowledge roles: seekers, contributors and brokers. We also distinguish between three types of selection mechanisms: objective selection mechanisms, feedback-based selection mechanisms and random selection mechanisms. By conducting agent-based simulations on four representative networks, i.e., regular networks, random networks, small-world networks and scale-free networks, our results show that the optimal knowledge diffusion performance can be achieved on scale-free networks where all agents implement objective mechanisms and show characteristics of brokers. Moreover, our results (a) highlight the significance of brokers, (b) illustrate the superiority of objective selection rules and (c) demonstrate that scale-free networks provide an optimal framework for knowledge diffusion. Furthermore, we also find the interdependent relevance of these three factors to knowledge diffusion and propose a qualitative explanation of these findings.     

产学研合作创新网络开放度对企业创新绩效的影响

利用中国国家知识产权局1999-2010年间ICT产业的专利数据,构建产学研合作创新网络,并提取2603家企业的大样本面板数据,探讨产学研合作创新网络中企业合作开放度对创新绩效的影响。研究结果表明：研发合作开放度与创新绩效间的关系存在一个边际合作效应递减的学习效应曲线的U型区域。从动态能力的角度看,开放度对创新绩效的作用可能会出现动态循环的态势,表现出类似波浪形的学习效应曲线。     

基于贝叶斯网络的组织创新影响机制研究

组织创新影响机制中各变量具有不确定性与动态性等特点,可以尝试进行贝叶斯网络分析。根据贝叶斯网络的基本原理,构建了基于贝叶斯网络的组织创新影响机制模型,并对复杂贝叶斯网络计算问题的简化问题进行了探讨。实例应用表明,该方法克服了其他传统分析方法局限于线性、静态分析的缺点,较为准确地反映了组织创新影响机制各变量间的动态关系。     

基于卷积神经网络的旅游信息关系抽取研究

[目的/意义]在非结构化语料集中抽取知识要素,是实现知识图谱的重要环节,本文探索了应用深度学习中的卷积神经网络（CNN）模型进行旅游领域知识关系抽取方法。[方法/过程]抓取专业旅游网站的相关数据建立语料库,对部分语料进行人工标注作为训练集和测试集,通过Python语言编程实现分词、向量化及CNN模型,进行关系抽取实验。[结果/结论]实验结果表明,应用卷积神经网络对非结构化的旅游文本进行关系抽取时能够取得满意的效果（Precision 0.77,Recall 0.76,F1-measure 0.76）。抽取结果通过人工校对进行优化后,可以为旅游知识图谱构建、领域本体构建等工作奠定基础。     

Promoting quality education in Chile: the politics of reforming teacher careers

ABSTRACT

Reformers in developing countries increasingly seek to raise education quality. Yet we know little about the politics of improving education. One significant and instructive case of reforms designed to boost education quality comes from Chile, where in 2016 the government enacted a sweeping reform of teaching careers. This paper first uses a quantitative analysis of appearances in the news to identify key stakeholders and then turns to process tracing to analyze how and when these stakeholders influenced reform dynamics. Comparatively, the Chilean case differs from similar reforms elsewhere in Latin America due to the absence of business, the strong role of policy networks, and the final negotiated settlement with the teacher union. Theoretically, the analysis confirms general theories that emphasize the roles of distributive politics and policy networks.     

Bayesian reasoning in avalanche terrain: a theoretical investigation

In this article, I explore a Bayesian approach to avalanche decision-making. I motivate this perspective by highlighting a version of the base-rate fallacy and show that a similar pattern applies to decision-making in avalanche-terrain. I then draw out three theoretical lessons from adopting a Bayesian approach and discuss these lessons critically. Lastly, I highlight a number of challenges for avalanche educators when incorporating the Bayesian perspective in their curriculum.     

The effect of toxicity on COVID-19 news network formation in political subcommunities on Reddit: An affiliation network approach

Political polarization remains perhaps the “greatest barrier” to effective COVID-19 pandemic mitigation measures in the United States. Social media has been implicated in fueling this polarization. In this paper, we uncover the network of COVID-19 related news sources shared to 30 politically biased and 2 neutral subcommunities on Reddit. We find, using exponential random graph modeling, that news sources associated with highly toxic – “rude, disrespectful” – content are more likely to be shared across political subreddits. We also find homophily according to toxicity levels in the network of online news sources. Our findings suggest that news sources associated with high toxicity are rewarded with prominent positions in the resultant network. The toxicity in COVID-19 discussions may fuel political polarization by denigrating ideological opponents and politicizing responses to the COVID-19 pandemic, all to the detriment of mitigation measures. Public health practitioners should monitor toxicity in public online discussions to familiarize themselves with emerging political arguments that threaten adherence to public health crises management. We also recommend, based on our findings, that social media platforms algorithmically promote neutral and scientific news sources to reduce toxic discussion in subcommunities and encourage compliance with public health recommendations in the fight against COVID-19.