电子研究在社会科学中的影响力：多元分析揭示实证

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本篇研究论文《衡量电子研究在社会科学中的影响》（Gauging the Impact of e-Research in the Social Sciences）由Eric T. Meyer和Ralph Schroeder共同撰写，两位作者均来自牛津互联网研究所，他们针对电子研究（e-Research）在社会科学领域的影响力进行了深入探讨。文章发表于2008年9月，是对英国e-Science All Hands Meeting上展示的研究成果的回应，并被邀请为《皇家学会哲学 Transactions A》的一期特刊撰稿。论文的核心方法是通过荟萃分析现有的资源，并结合新收集的数据，系统地评估电子研究工具在社会科学领域的实际应用和认知程度。研究的主要来源包括两份针对社会科学家的在线调查，这些调查旨在量化他们对电子研究工具的使用频率和理解度。此外，作者还参考了国家电子社会科学计划的报告，以及科学计量分析的结果，以全面了解电子研究如何影响科学研究的实践和理论发展。论文强调了多维度分析的重要性，因为电子研究不仅改变了研究工具和方法，还可能影响跨学科的合作、数据获取与处理、研究过程的透明度以及研究成果的传播方式。作者们试图揭示不同层面的指标，如技术接受度、研究效率提升、科研产出质量的提升以及跨学科交流的加强等，来更公正地评估电子研究对社会科学领域的深度变革。电子版本可在http://ssrn.com/abstract=1262213找到。该研究不仅提供了实证数据支持，还对未来电子研究在社会科学中的进一步发展提出了潜在的理论框架和实践建议，对于理解电子科技如何重塑学术研究格局具有重要意义。

certain social sciences (sociology, geography and psychology) rather than others (anthropology, economics

and political science).

So, for example, in the UK, the e-Social Science initiative is thus focused on some types of social sciences

rather than others. There are also national variations. For example, in the UK, there has been a major focus

on e-Social Science, though with little emphasis on business and economics, while in the German D-Grid

initiative, e-Research focuses almost exclusively on business applications without any other social sciences.

In other areas of social science, despite an ambitious e-Research programme that includes many natural

science and humanities projects, the uptake in the social sciences has so far been conspicuous by its absence

in Germany (Schroeder, Den Besten, & Fry, 2007). In Sweden, there was initially a plan to develop e-

Infrastructure to support a wide range of social science research, but subsequently there has come to be a

narrower focus on creating a facility for sharing micro-data (Axelsson & Schroeder, 2007). In the EU, within

the European Strategy Forum on Research Infrastructures (ESFRI), the main effort has been to develop

infrastructures around existing large-scale quantitative datasets such as the European Social Science Data

Archives and the European Social Survey. These kinds of efforts create commonalities across national

programmes, such as when national social science datasets that are being federated via CESSDA (Council of

European Social Science Data Archives) and similar organizations around the world.

In the US, two flagship projects have been funded by the Office of the Cyberinfrastructure of the National

Science Foundation, one focusing on social network structure of the Web and the other collecting real-time

multimodal behavioural data. Finally, in the UK, the ‘nodes’ of the National Centre for e-Social Science

programme have concentrated on certain areas within the social sciences (such as qualitative and quantitative

sociology) and not others (such as anthropology and political science). These national research programmes

and funding initiatives will, at a minimum, shape the kinds of infrastructures and communities of researchers

that develop in e-Research.

There is also the question of what types of data or other kinds of phenomena lend themselves to

transformation into - or representation in - digital form. Moreover, the phenomena that can be captured or

represented in this way must be transferable, manipulable, and shareable via networks. This raised questions

of security, privacy and trust when sensitive quantitative (Axelsson & Schroeder, 2007) and qualitative (see,

for example, Jirotka et al., 2005) data about human subjects is made accessible for distributed collaboration.

2.2 Research Technologies

One impact of e-Research on the social sciences is to make them increasingly reliant on research

technologies. It is important to spell out what this impact means. First, it has been argued that research

technologies have been crucial to the rise of modern science. Thus Collins argues that research technologies

led to the rise of ‘high-consensus rapid-discovery science’ (1998, pp. 532-538) from about 1600 onwards in

Europe. This is an important argument inasmuch as it stands the conventional wisdom – that progress in

science leads to more powerful technologies – on its head. Instead, new technologies for research lead, in

Collins’ view, to progress in scientific knowledge. It is easy to think of examples: new and improved

telescopes, microscopes, galvanometers – or today, computers – lead to scientific discoveries because they

allow more powerful representations and manipulations of the physical world.

The reason why research technologies make for ‘high-consensus rapid-discovery science’ is that they make it

possible for scientists to use equipment to manipulate phenomena and improve the research equipment to do

this – and move on to new phenomena: ‘What was discovered was a method of discovery; confidence was

soon built up that techniques could be modified and recombined endlessly, with new discoveries guaranteed

continually along the way. And the research technologies gave a strong sense of the objectivity of the

phenomena, since they were physically demonstrable. The practical activity of perfecting each technique

consisted in modifying it until it would reliably repeat the phenomena at will’ (Collins, 1994, p. 163).

Does the same apply to social science? Collins argues that it does, but to a far lesser extent. This is because

the social sciences have been slow to adopt the use of research technologies. Here we can think, for example,

of the use of recording technologies in sociology, which has become commonplace quite recently, partly

because of the expense and quality and bulkiness of recording equipment (R. M. Lee, 2004). Similarly if we

consider the uses of computers for large population datasets: among the earliest users of commercial

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