"Ensuring Legacy Data Access & Dissemination: Occupational Coding In The General Social Survey"

Michael Hout，加州大学伯克利分校和国家意见研究中心/NORC Peter Marsden，哈佛大学和国家意见研究中心/NORC确保遗留数据访问传播：一般社会调查中的职业代码摘要在过去的三十年里，技术、商业和政府实践的变化已经大大改变了美国的职业结构。我们的项目为理解新职业对当前经济和当代社会的影响提供了基础，并保存了记录这些根本性历史变化的独特数据。具体而言，该项目使1970年代至今的社会普查中的职业和行业数据现代化。 该项目有几个目标。它们与NSF最近的关键建议相吻合，这些建议鼓励GSS等大型基础设施数据源促进数据访问和传播。 这可以通过根据明确定义的协议提供数据和元数据来实现，这将允许采用理想的数据访问、搜索、下载和记录模式。 该项目还满足了NSF的挑战，改造历史或遗留数据和元数据，使其成为机器可读的。 一旦保密和披露问题得到解决，这可能会开放大量数据供传播和分析。为了实现这一目标，本项目将（1）检索GSS受访者？他们的工作活动，职业和行业的详细逐字描述，从早期的GSS波的物理问卷手稿，（2）将它们转换为机器可读的形式，（3）重新编码，以反映2010年的职业和2007年的行业类别由美国人口普查开发，（4）附加外部数据，如社会经济评分和声望评估重新编码的类别。在这一过程中将职业信息数字化并对职业和行业类别进行重新编码的智力价值在于，它使研究人员能够充分利用GSS中记录的职业和行业信息的潜力。 这样做将提高GSS作为社会不平等，流动性和其他领域的比较和当代研究资源的价值，并保持其作为描述美国两代人社会趋势的历史数据库的日益增长的价值。通过将手写文本转换为机器可读文本，确保这些遗留数据的寿命，该项目还开发了一个逐字描述的存档，使未来的研究人员能够使用其他标准对它们进行编码，包括可能在未来几十年内可用的美国人口普查标准。更广泛的影响GSS是一种公共资源，也是一种科学资源。公共媒体，特别是报纸，广泛使用安全总局。该项目通过提高通用统计系统中职业和行业信息的质量，并确保这些信息的编码方式始终一致，将有助于记者和公民了解社会趋势和模式。此外，高中和大学广泛使用GSS作为教学工具。当数据尽可能准确和精确地反映职业和行业之间的当代差异时，教师和学生将从该项目产生的新数据产品中获得更多的练习。

SES - 1123510Michael Hout, University of California, Berkeley, and National Opinion Research Center/NORCPeter Marsden, Harvard University and National Opinion Research Center/NORCEnsuring Legacy Data Access & Dissemination: Occupational Codes in the General Social SurveyAbstractIn the past thirty years, changes in technology, business, and government practice have substantially altered the American occupational structure. Our project provides a foundation to understand the consequences of new occupations on the current economy and contemporary society, and to preserve unique data key to documenting these fundamental historical changes. Specifically, this project modernizes the occupational and industry data in the General Social Survey (GSS) from the 1970s to the present time. The project has several goals. They dovetail recent key NSF recommendations that encourage large infrastructure data sources such as the GSS to facilitate increased data access and dissemination. This can be done by presenting data and metadata according to a well-defined protocol, which will allow desirable modes of data access, search, downloads, and documentation. The project also meets the NSF challenge to retrofit historical or legacy data and metadata to become machine readable. This will possibly open up vast amount of data for dissemination and analysis once issues of confidentiality and disclosure are resolved. To accomplish this goal, this project will (1) retrieve GSS respondents? detailed verbatim descriptions of their work activities, occupations, and industries from the physical questionnaire manuscripts from early GSS waves, (2) convert them into machine-readable form, (3) recode them to reflect 2010 occupation and 2007 industry categories developed by the U.S. Census, and (4) attach external data such as socioeconomic scores and prestige assessments to the recoded categories. The intellectual merit of digitizing occupational information and recoding occupational and industry categories in the process is that it enables researchers to use the full potential of the occupation and industry information recorded in the GSS over time. Doing so will enhance the value of the GSS as a resource for comparative and contemporary research on social inequality, mobility, and other fields and preserve its growing value as a historical database describing trends in U.S. society over two generations. Ensuring the longevity of such legacy data by converting hand-written text into machine-readable text, the project also develops an archive of verbatim descriptions that will allow future researchers to code them using other standards, including U.S. Census standards that may become available in upcoming decades.Broader ImpactsThe GSS is a public resource as well as a scientific one. Public media, especially newspapers, make extensive use of the GSS. By improving the quality of occupational and industry information in the GSS and ensuring that it is coded in a consistent way over time, this project will help journalists and citizens make sense of social trends and patterns. Also, high schools and colleges make extensive use of the GSS as a teaching tool. Teachers and students will get more out of these exercises from the new data products this project will produce when data reflect contemporary distinctions among occupations and industries as accurately and precisely as possible.