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来源类型Working Paper
规范类型报告
DOI10.3386/w28431
来源IDWorking Paper 28431
Man Versus Machine? Self-Reports Versus Algorithmic Measurement of Publications
Xuan Jiang; Wan-Ying Chang; Bruce A. Weinberg
发表日期2021-02-08
出版年2021
语种英语
摘要This paper uses newly available data from Web of Science on publications matched to researchers in Survey of Doctorate Recipients to compare scientific publications collected by surveys and algorithmic approaches. We aim to illustrate the different types of measurement errors in self-reported and machine-generated data by estimating how publication measures from the two approaches are related to career outcomes (e.g. salaries, placements, and faculty rankings). We find that the potential biases in the self-reports are smaller relative to the algorithmic data. Moreover, the errors in the two approaches are quite intuitive: the measurement errors of the algorithmic data are mainly due to the accuracy of matching, which primarily depends on the frequency of names and the data that was available to make matches; while the noise in self reports is expected to increase over the career as researchers’ publication records become more complex, harder to recall, and less immediately relevant for career progress. This paper provides methodological suggestion on evaluating the quality and advantages of two approaches to data construction. It also provides guidance on how to use the new linked data.
主题Econometrics ; Estimation Methods ; Labor Economics ; Labor Supply and Demand ; Labor Compensation ; Development and Growth ; Innovation and R& ; D
URLhttps://www.nber.org/papers/w28431
来源智库National Bureau of Economic Research (United States)
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资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/586104
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Xuan Jiang,Wan-Ying Chang,Bruce A. Weinberg. Man Versus Machine? Self-Reports Versus Algorithmic Measurement of Publications. 2021.
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