中國大陸(Mainland China)做為社會科學學術研究對象,隨學科變遷以及兩岸情勢發展,在各時期有不同主題。承此,本文使用主題分析工具 (CATAR),對「中國大陸研究」期刊於 1998~2015 年刊載之論文,透過論文的篇名與摘要文字,從事主題群聚(clustering)分析,藉以辨識顯著的研究主題,及其關鍵字,並以此觀察各主題發展趨勢。結果呈現出「中國大陸研究」之 473 篇文章,可歸類為七大主題,每一主題各有關鍵字。從每個主題的發表量(包括「發表數量」、「發表數量百分率」)之變化,可看出歷年期刊(或研究者)偏好主題之演進。從趨勢可以看出,存在兩個主流議題,其他主題的年度篇數則變化較大。本研究貢獻為:1.對「各篇獨立」的研究找到共通主題之可能性,從少量樣本的分群實驗,結果與常識相符,驗證了自動化主題分析的可行性。2.呈現了臺灣學術界在「中國大陸研究」方面的研究關鍵字發展趨勢。3.擷取出各個主題的關鍵字,提供未來研究者方便查閱過去的研究趨向。4.根據逐年的趨勢演變,呈現後續研究的主題方向。
With the rapid development of cross-strait situation, “Mainland China” as a subject of social science studies reflects different topics in different eras. This study applies an automatic content analysis tool(CATAR)to analyze the journal “Mainland China Studies”(1998-2015)to observe research trends based on clustering of the texts from the title and abstract of each journal article. The results show that the 473 articles published by the journal are clustered into seven salient topics. By publication number of each topic over time(including “volume of publications”, “percentage of publications”), we observe two persistent, and five variable topics. Our study demonstrates (1)feasibility of grouping related articles into meaningful topics,(2) trends of research topics in the journal “Mainland China Studies”,(3) topical keywords that provide easy access to past studies,(4)future research directions as signified by yearly trends.
厚資料(thick data)這個名詞大約在 2013 到 2014 年間被創造出來。先在網路上流傳,後來出現在管理學的評論及期刊之中。一開始,這個詞的意思是強調「質化」方法的知識建構,多是從人類學的視角出發。但這並不新。其實,「厚」的核心內涵很早就在人類學中被運用,原稱叫作厚實描述(thick description),因此,現在使用「厚資料」一詞者,不少是從「厚描述」或「厚敘事」(thick descriptions)的人類學民族誌研究方法(ethnog..
As a reflection and supplement to data-driven research, thick data was firstly proposed as a complementary method of using data to engage in meaning mining in 2013. Through the case of Chinese political economy, this article demonstrates how the use of thick data enables researchers to overcome the problem of data distortion. It argues that meaningful use of data sources is based on the identification of actors. In order to do so, researchers are required to answer the following two questions: Who are the actors contributing to the tendency..
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