Thick Big Data

D. Jemielniak
{"title":"Thick Big Data","authors":"D. Jemielniak","doi":"10.1093/oso/9780198839705.001.0001","DOIUrl":null,"url":null,"abstract":"The social sciences are becoming datafied. The questions that have been considered the domain of sociologists, now are answered by data scientists, operating on large datasets, and breaking with the methodological tradition for better or worse. The traditional social sciences, such as sociology or anthropology, are thus under the double threat of becoming marginalized or even irrelevant; both because of the new methods of research, which require more computational skills, and because of the increasing competition from the corporate world, which gains an additional advantage based on data access. However, sociologists and anthropologists still have some important assets, too. Unlike data scientists, they have a long history of doing qualitative research. The more quantified datasets we have, the more difficult it is to interpret them without adding layers of qualitative interpretation. Big Data needs Thick Data. This book presents the available arsenal of new tools for studying the society quantitatively, but also show the new methods of analysis from the qualitative side and encourages their combination. In shows that Big Data can and should be supplemented and interpreted through thick data, as well as cultural analysis, in a novel approach of Thick Big Data.The book is critically important for students and researchers in the social sciences to understand the possibilities of digital analysis, both in the quantitative and qualitative area, and successfully build mixed-methods approaches.","PeriodicalId":156019,"journal":{"name":"Research Methods for Digital Work and Organization","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research Methods for Digital Work and Organization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/oso/9780198839705.001.0001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 22

Abstract

The social sciences are becoming datafied. The questions that have been considered the domain of sociologists, now are answered by data scientists, operating on large datasets, and breaking with the methodological tradition for better or worse. The traditional social sciences, such as sociology or anthropology, are thus under the double threat of becoming marginalized or even irrelevant; both because of the new methods of research, which require more computational skills, and because of the increasing competition from the corporate world, which gains an additional advantage based on data access. However, sociologists and anthropologists still have some important assets, too. Unlike data scientists, they have a long history of doing qualitative research. The more quantified datasets we have, the more difficult it is to interpret them without adding layers of qualitative interpretation. Big Data needs Thick Data. This book presents the available arsenal of new tools for studying the society quantitatively, but also show the new methods of analysis from the qualitative side and encourages their combination. In shows that Big Data can and should be supplemented and interpreted through thick data, as well as cultural analysis, in a novel approach of Thick Big Data.The book is critically important for students and researchers in the social sciences to understand the possibilities of digital analysis, both in the quantitative and qualitative area, and successfully build mixed-methods approaches.
厚大数据
社会科学正在变得数据化。这些问题一直被认为是社会学家的领域,现在由数据科学家来回答,他们在大数据集上操作,无论好坏,都打破了方法论的传统。传统的社会科学,如社会学或人类学,因此面临着被边缘化甚至无关紧要的双重威胁;一方面是因为新的研究方法需要更多的计算技能,另一方面是因为来自企业界的竞争日益激烈,企业界在数据访问方面获得了额外的优势。然而,社会学家和人类学家仍然有一些重要的资产。与数据科学家不同,他们从事定性研究的历史很长。我们拥有的量化数据集越多,就越难以在不添加定性解释层的情况下解释它们。大数据需要厚数据。这本书提出了可用的兵工厂的新工具,研究社会定量,但也显示了新的分析方法,从定性方面,并鼓励他们的组合。这表明大数据可以而且应该通过厚数据和文化分析来补充和解释,以一种新颖的厚大数据方法。这本书是至关重要的学生和研究人员在社会科学了解数字分析的可能性,无论是在定量和定性领域,并成功建立混合方法的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信