Statistical Genre Analysis: Toward Big Data Methodologies in Technical Communication

被引：29

作者：

Graham, S. Scott ^{[1
]}

Kim, Sang-Yeon ^{[2
]}

DeVasto, Danielle M. ^{[3
]}

Keith, William ^{[4
]}

机构：

[1] Univ Wisconsin Milwaukee, Sci & Med Commun Lab, Milwaukee, WI 53211 USA

[2] Univ Wisconsin Milwaukee, Dept Commun, Milwaukee, WI USA

[3] Univ Wisconsin Milwaukee, Rhetor & Composit, Dept English, Milwaukee, WI USA

[4] Univ Wisconsin Milwaukee, Rhetor, Milwaukee, WI USA

来源：

TECHNICAL COMMUNICATION QUARTERLY | 2015年 / 24卷 / 01期

关键词：

big data; genre analysis; pharmaceuticals; quantitative methods; science policy;

D O I：

10.1080/10572252.2015.975955

中图分类号：

G2 [信息与知识传播];

学科分类号：

05 ; 0503 ;

摘要：

This article pilots a study in statistical genre analysis, a mixed-method approach for (a) identifying conventional responses as a statistical distribution within a big data set and (b) assessing which deviations from the conventional might be more effective for changes in audience, purpose, or context. The study assesses pharmaceutical sponsor presentations at the Food and Drug Administration (FDA) drug advisory committee meetings. Preliminary findings indicate the need for changes to FDA conflict-of-interest policies.

引用

页码：70 / 104

页数：35

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