Illegal drug is a serious global problem today. It is necessary to understand its distribution and trafficking routes in order to tackle this problem. Moreover, it is estimated that approximately 60 percent of the illegal drugs in Taiwan came from overseas. Hence, the flaw of cross-border drug control is also an important issue in international affairs. This article uses legal analytics to study 71,629 judgements text involving drug-related crimes to grasp the picture of case factor and structure by text mining technology. It is found that two-thirds of the above-mentioned cases were recidivists, and three-fourths of the total cases were subject to a fine at first. Secondly, the type of drug-related crimes keeps changing during different time periods. The types of drug-related crimes from most common to least common are as follows: use, possession, produc- tion, sale, transfer, transportation, and cultivation. Thirdly, the third-level drug, Ketamine and the fourth-level drug including raw materials all came from overseas, but most of the drugs being used and possessed are the second-level drugs. Fourthly, by SEM(Structural Equation Modelling), this article points out that the most frequently seen characteristics of cross-border drug flaw is that the sale of goods is positively correlated with the type of the drugs, which illustrates that the main drug transportation route is from cargo by air or sea or smuggling. The research also discovered the main structure of its flaw is that the raw materials, often categorized as the fourth-level drug, are smuggled from mainland China, and then manufactured locally to be second-level drugs for export to Japan or other developed countries. In other words, Taiwan has pos- sibly become a node of drug production in the perspective of the global drug trade chain. Lastly, this research has successfully applied text-mining to the large amount of legal texts, which is undoubtedly Think Data, to retrieve useful information and shows that legal analytics is possible and practical nowadays.
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 mean- ing mining in 2013. Through the case of Chinese political economy, this ar- ticle 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 contribut..
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