分项校对法在识别编排规范性错误中的应用

校对是出版工作中必不可少的一个重要环节,校对质量是构成出版物质量的重要因素之一[1].校对的最终目的在于消灭出版物中一切可能发生的错误与缺陷,从而提高出版物的质量.科技期刊中公式、符号多,符号著录又有不同的文种、上角下角、正体斜体,相当复杂,因此,对校对工作的要求就更加严格[2].科技期刊要走向世界,编排的标准化规范化是一大举措[3].<护理学杂志>由编辑人员兼做校对工作,常规进行3次校对及1次印前审读.笔者将分项校对法作为常规校对的补充,发现对识别编排规范性错误收效甚好.     

Gender-specific patterns in the artificial intelligence scientific ecosystem

Gender disparity in science is one of the most focused debating points among authorities and the scientific community. Over the last few decades, numerous initiatives have endeavored to accelerate gender equity in academia and research society. However, despite the ongoing efforts, gaps persist across the world, and more measures need to be taken. Using social network analysis, natural language processing, and machine learning, in this study, we comprehensively analyzed gender-specific patterns in the highly interdisciplinary and evolving field of artificial intelligence for the period of 2000–2019. Our findings suggest an overall increasing rate of mixed-gender collaborations. From the observed gender-specific collaborative patterns, the existence of disciplinary homophily at both dyadic and team levels is confirmed. However, a higher preference was observed for female researchers to form homophilous collaborative links. Our core-periphery analysis indicated a significant positive association between having diverse collaboration and scientific performance and experience. We found evidence in support of expecting the rise of new female superstar researchers in the artificial intelligence field.     

论人工智能应用给学术期刊编辑带来的机遇与挑战

从编辑实践角度出发,探讨人工智能应用对学术期刊编辑工作的影响并分析了学术期刊编辑的转型路径。人工智能带来的机遇有：优化学术出版工作流程并提高加工质量、强化编辑把关能力和效果、AI生成内容给编辑从事全媒体传播活动提供了机遇、提升学术出版资源配置和编辑价值创造力。但人工智能也带来如何与人工智能技术协调、面对人工智能生产内容、人工智能生成的新媒体形态等挑战。期刊编辑需要锻造数据分析和价值发现能力、提高智能环境下的把关能力、形成与技术协同的全媒体编辑能力和以人文价值驾驭新技术的新能力应对上述挑战。     

Using scenarios to identify and manage allocation decision risks

This paper employs the solver function in Microsoft Excel? to make a ‘what-if' analysis of library materials budgetary allocation decisions. Two sets of allocation constraints are presented and used to describe two different theoretical scenarios: (1) a slight materials budget increase and (2) a significant materials budget decrease. In each scenario, the associated allocation ‘parity' risks are identified using solver and the outcomes of different risk management strategies are considered. Sensitivity to context requires different risk management solutions to minimize conflicts between stakeholders with competing interests. By pre-managing allocation risks, the decisions allow library materials budgets to be optimized.     

Using contextual models to configure insurance products and contracts

Dynamic changes in the global insurance market require the rapid development of new insurance products. This paper describes the designed context-based conceptual model of an insurance product and integrated scenarios of configuration of insurance products and policies. The presented results were used for the implementation of the “Insurer’s Internet Front Office” software which enables the introduction of brand new insurance products in a short period of time.     

Assessing behavioral data science privacy issues in government artificial intelligence deployment

In today's global culture where the Internet has established itself as the main tool for communication and commerce, the capability to massively analyze and predict citizens' behavior has become a priority for governments in terms of collective intelligence and security. At the same time, in the context of novel possibilities that artificial intelligence (AI) brings to governments in terms of understanding and developing collective behavior analysis, important concerns related to citizens' privacy have emerged. In order to identify the main uses that governments make of AI and to define citizens' concerns about their privacy, in the present study, we undertook a systematic review of the literature, conducted in-depth interviews, and applied data-mining techniques. Based on our results, we classified and discussed the risks to citizens' privacy according to the types of AI strategies used by governments that may affect collective behavior and cause massive behavior modification. Our results revealed 11 uses of AI strategies used by the government to improve their interaction with citizens, organizations in cities, services provided by public institutions or the economy, among other areas. In relation to citizens' privacy when AI is used by governments, we identified 8 topics related to human behavior predictions, intelligence decision making, decision automation, digital surveillance, data privacy law and regulation, and the risk of behavior modification. The paper concludes with a discussion of the development of regulations focused on the ethical design of citizen data collection, where implications for governments are presented aimed at regulating security, ethics, and data privacy. Additionally, we propose a research agenda composed by 16 research questions to be investigated in further research.     

Barriers to artificial intelligence adoption in smart cities: A systematic literature review and research agenda

Artificial intelligence (AI) plays a prominent role in smart cities' development and offers benefits to different services such as finance, healthcare, security, agriculture, transport, education, and manufacturing. Despite the expected benefits, the adoption of AI varies from one smart city to another, due in part to barriers that can inhibit a smart city from adopting AI. The aim of this paper is to provide a comprehensive view of the barriers faced by smart cities. Through a systematic literature review, this study identifies 18 primary and secondary barriers grouped into three main categories — technology, environment, and organization. This research contributes to the literature by developing a typology of AI adoption barriers based on the Technology-Organization-Environment (TOE) perspective. The typology provides a novel mapping of the barriers to AI adoption faced by smart cities and suggests directions for further investigation through a cohesive research agenda. At a practical level, the findings will allow policymakers, planners, and citizens to make more informed decisions about AI adoption. Practical implications are also proposed for guiding smart cities to increase the adoption of AI.     

新世纪国际人工智能研究领域可视化分析

目的：全面了解新世纪国际人工智能领域的研究现状与研究热点。方法：运用TDA软件,利用文献统计分析、关键词共现分析的方法揭示研究热点。结果：国际人工智能领域文献量呈上升趋势,其中美国发文量排名第一,中国位居第六。人工智能的主研究领域包括计算机科学、工程学、自动化与控制系统3个学科。研究热点为遗传算法、神经网络和机器学习。结论：新世纪国际人工智能研究涉及多个学科,研究热点集中在知识获取、知识表示和问题求解3个宏观层面,神经网络、遗传算法和机器学习是研究者们关注的核心。     

人工智能在档案资源开发利用中的应用分析

人工智能的应用为档案资源开发利用带来机遇,但目前在档案领域的应用尚处于探索时期。一方面,智能语音技术、光学字符识别技术、人脸识别技术等能够促进档案资源的文本化、数字化与检索智能化;另一方面,档案资源类型复杂、指导理念与技术脱节、用户信息泄露、算法伦理等问题制约着其发挥作用。本文通过分析人工智能在档案资源开发利用中的现状,探讨其在资源层面、理念层面与技术层面遇到的困境,提出通过技术聚焦、理念更新、用户信息加密等策略全面优化人工智能在档案资源开发利用中的应用,为充分挖掘与实现档案价值赋能。     

Signatures of capacity development through research collaborations in artificial intelligence and machine learning

Extant studies suggest that the proximity between the researchers and their structural positioning in the collaboration network may influence productivity and performance in collaboration research. In this paper, we analyze the co-authorship networks of the three countries, viz. the USA, China, and India, constructed in consecutive non-overlapping 5-year long time windows from bibliometric data of research papers published in the past decade in the rapidly evolving area of Artificial Intelligence and Machine Learning (AI&ML). Our analysis relies on the observations ensued from a comparison of the statistical properties of the evolving networks. We consider macro-level network properties which describe the global characteristics, such as degree distribution, assortativity, and large-scale cohesion etc., as well as micro-level properties associated with the actors who have assumed central positions, defining a core in the network assembly with respect to closeness centrality measure. For the analysis of the core actors, who are well connected with a large number of other actors, we consider share of their affiliations with domestic institutes. We find dominant representation of domestic affiliations of the core actors for high productivity cases, such as China in the second time window and the USA in the first and second both. Our study, therefore, suggests that the domestic affiliation of the core actors, who could access network resources more efficiently than other actors, influences and catalyzes the collaborative research.     

From E-budgeting to smart budgeting: Exploring the potential of artificial intelligence in government decision-making for resource allocation

Artificial intelligence has become an important tool for governments around the world. However, it is not clear to what extent artificial intelligence can improve decision-making, and some policy domains have not been the focus of most recent studies, including the public budget process. More specifically, budget allocation is one of the areas in which AI may have greatest potential. Therefore, this study attempts to contribute to this gap in our existing knowledge by answering the following research question: To what extent can artificial intelligence techniques help distribute public spending to increase GDP, decrease inflation and reduce the Gini index? In order to respond to this question, this article proposes an algorithmic approach on how budget inputs (specific expenditures) are processed to generate certain outputs (economic, political, and social outcomes). The authors use the multilayer perceptron and a multiobjective genetic algorithm to analyze World Bank Open Data from 1960 to 2019, including 217 countries. The advantages of implementing this type of decision support system in public expenditures allocation arise from the ability to process large amounts of data and to find patterns that are not easy to detect, which include multiple non-linear relationships. Some technical aspects of the expenditure allocation process could be improved with the help of these kinds of techniques. In addition, the results of the AI-based approach are consistent with the findings of the scientific literature on public budgets, using traditional statistical techniques.     

人工智能背景下科技论文摘要的机器翻译与译后编辑

神经网络机器翻译的发展为作者英译科技论文摘要提供了便利,也丰富了科技期刊英文编辑的工作内涵。通过借鉴(GB/T 40036—2021)《翻译服务 机器翻译结果的译后编辑要求》中对机器翻译译后编辑类型、任务及目标等相关问题的界定来探讨机译英文摘要的编辑加工方法不失为一次全新而有益的尝试。本文辨析了作者、编辑进行机译英文摘要译后编辑的不同类型,阐述了编辑层面的译后编辑任务分解流程,归纳了机译英文摘要的常见错误类型,探讨了与摘要内容相适应的译后编辑策略,并认为编辑应坚守人机互动翻译模式中的主导地位。     

The moon,the ghetto and artificial intelligence: Reducing systemic racism in computational algorithms

Computational algorithms and automated decision making systems that include them offer potential to improve public policy and organizations. But computational algorithms based on biased data encode those biases into algorithms, models and their outputs. Systemic racism is institutionalized bias with respect to race, ethnicity and related attributes. Such bias is located in data that encode the results and outputs of decisions that have been discriminatory, in procedures and processes that may intentionally or unintentionally disadvantage people based on race, and in policies that may discriminate by race. Computational algorithms may exacerbate systemic racism if they are not designed, developed, and used–that is, enacted–with attention to identifying and remedying bias specific to race. Advancing social equity in digital governance requires systematic, ongoing efforts to assure that automated decision making systems, and their enactment in complex public organizational arrangements, are free from bias.     

Perceptions of artificial intelligence: A survey of academic librarians in Canada and the United States

This study aims to evaluate the perceptions of librarians with regard to artificial intelligence in academic libraries. An online survey of 24 questions was distributed through library distribution lists in Canada and the United States at the end of the summer in 2019. Findings suggest that librarians do not agree on a definition of artificial intelligence which is in keeping with this emerging field. The survey responses highlight the fact that academic librarians require more training with regard to artificial intelligence and its potential applications in libraries. Other important implications include a recognition that library patrons are interested in AI and that little to no programming about it has been offered in academic libraries. Very few studies have focused on academic librarians' perceptions of AI. This article highlights some useful practical implications for AI technologies in libraries and how AI could help improve library services and workflows.     

J.S. Mill’s inductive methods in artificial intelligence systems. Part I

The principles of J.S. Mill’s induction are formulated. These principles are developed and formalized for the five J.S. Mill’s inductive methods (the method of agreement, the method of difference, the joint method of agreement and difference, the method of residues, and the method of concomitant variations). The method of agreement, the method of difference, and the joint method of agreement and difference are considered in Part I. The possible strategies of plausible reasoning that realize the interaction of Mill’s induction, analogy, and abduction are formulated. These strategies are considered to be cognitive reasoning (justification is provided).     

“小”差错，大问题——科技期刊中的政治性差错举隅

为了提高科技期刊编辑对政治性差错的感性认识,引起科技期刊编辑的重视,避免各种类型政治性差错的发生,收集整理了源自中国科学引文数据库来源期刊的一些有代表性的政治性差错。科技期刊中的政治性差错貌似细小,实则严重。科技期刊中的政治性差错存在于科技论文的各个部分,包括题名、作者单位、正文、插图、表格、参考文献等。科技期刊中的政治性差错涵盖了所有类型的政治性差错。     

Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda

To lay the foundation for the special issue that this research article introduces, we present 1) a systematic review of existing literature on the implications of the use of Artificial Intelligence (AI) in public governance and 2) develop a research agenda. First, an assessment based on 26 articles on this topic reveals much exploratory, conceptual, qualitative, and practice-driven research in studies reflecting the increasing complexities of using AI in government – and the resulting implications, opportunities, and risks thereof for public governance. Second, based on both the literature review and the analysis of articles included in this special issue, we propose a research agenda comprising eight process-related recommendations and seven content-related recommendations. Process-wise, future research on the implications of the use of AI for public governance should move towards more public sector-focused, empirical, multidisciplinary, and explanatory research while focusing more on specific forms of AI rather than AI in general. Content-wise, our research agenda calls for the development of solid, multidisciplinary, theoretical foundations for the use of AI for public governance, as well as investigations of effective implementation, engagement, and communication plans for government strategies on AI use in the public sector. Finally, the research agenda calls for research into managing the risks of AI use in the public sector, governance modes possible for AI use in the public sector, performance and impact measurement of AI use in government, and impact evaluation of scaling-up AI usage in the public sector.     

Benchmarking in health care: using the Internet to identify resources

Benchmarking is a quality improvement tool that is increasingly being applied to the health care field and to the libraries within that field. Using mostly resources assessible at no charge through the Internet, a collection of information was compiled on benchmarking and its applications. Sources could be identified in several formats including books, journals and articles, multi-media materials, and organizations.