Читать книгу An Introduction to Text Mining - Gabe Ignatow - Страница 20

The philosopher and historian Foucault (1973) developed an influential conceptualization of intertextuality that differs significantly from Fairclough’s conceptualization in CDA. Rather than identifying the influence of external discourses within a text, for Foucault the meaning of a text emerges in reference to discourses with which it engages in dialogue. These engagements may be explicit or, more often, implicit. In Foucauldian intertextual analysis, the analyst must ask each text about its presuppositions and with which discourses it dialogues. The meaning of a text therefore derives from its similarities and differences with respect to other texts and discourses and from implicit presuppositions within the text that can be recognized by historically informed close reading.

Foucauldian analysis of texts is performed in many theoretical and applied research fields. For instance, a number of studies have used Foucauldian intertextual analysis to analyze forestry policy (see Winkel, 2012, for an overview). Researchers working in Europe (e.g., Berglund, 2001; Franklin, 2002; Van Herzele, 2006), North America, and developing countries (e.g., Asher & Ojeda, 2009; Mathews, 2005) have used Foucauldian analysis to study policy discourses regarding forest management, forest fires, and corporate responsibility.

Another example of Foucauldian intertextual analysis is a sophisticated study of the professional identities of nurses by Bell, Campbell, and Goldberg (2015). Bell and colleagues argued that nurses’ professional identities should be understood in relation to the identities of other occupational categories within the health care field. The authors collected their data from PubMed, a medical research database. Using PubMed’s own user interface, the authors acquired the abstracts for research papers that used the terms service or services in the abstract or key words for a period from 1986 to 2013. The downloaded abstracts were added to an SQLite database, which was used to generate comma-separated values (CSV) files with abstracts organized into 3-year periods. The authors then spent approximately 6 weeks of full-time work, manually checking the data for duplicates and other errors. The final sample included over 230,000 abstracts. Bell and colleagues then used the text analysis package Leximancer (see Appendix C) to calculate frequency and co-occurrence statistics for all concepts in the abstracts (see also Appendix F). Leximancer also produced concept maps (see Appendix G) to visually represent the relationships between concepts. The authors further cleaned their data after viewing these initial concept maps and finding a number of irrelevant terms and then used Leximancer to analyze the concept of nursing in terms of its co-occurrence with other concepts.