Titolo della tesi: The standardisation of suffixes and definitions in noun taxonomies: a preliminary corpus-based and data-based analysis of the early modern English microlanguage of economics (1572-1664)
In the 16th and 17th centuries, economics was still not a proper discipline. The trade crisis in the early 1620s, and the international political economy pursued by the trading activities of the companies, have encouraged a new debate on the economy as a system. In this scenario, English required a new specialised lexicon to discuss and analyse these fields from a new empirical observation. The extensive derivatives and borrowings from Latin and the uncodified strategies for the definition of the new terms make the analysis of early modern economics of paramount importance for historical corpus linguistics.
This dissertation aims to present the research conducted on the early modern English microlanguage of economics between 1572 and 1664. Particular reference has been devoted to the standardisation of suffixes and definitions in noun taxonomies. These phenomena have been explored from the perspectives of the diachronic variation and functional elaboration of this diaphasic variety.
For the purposes of this project, a sample corpus of texts written by early economists in the reference period has been compiled. The sample has been queried with the corpus-based software #Lancsbox and compared with a reference sub-corpus extracted via CQPweb. The massive spelling variation, i.e. a discrete trait of early modern English, has been handled by normalising the sample with the software package VARD 2.
This research aims to exploit the opportunities derived from historical corpus linguistics as a method. Firstly, a quantitative analysis has been conducted to test the sample with data-based and statistical analyses. The three software packages have been used to find new types of data which may contribute to the analysis of the early modern English economics discourse via corpora. Secondly, a qualitative analysis has been carried out to retrieve patterns of usage and development, and to categorise them with the contribution of multiple predictor variables.