ARCHAEOLOGY IN CANADA: AN ANALYSIS OF DEMOGRAPHICS AND WORKING CONDITIONS IN THE DISCIPLINE by © Catherine L. Jalbert A Dissertation submitted to the School of Graduate Studies in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Archaeology, Faculty of Humanities and Social Sciences Memorial University of Newfoundland May 2019 St. John’s, Newfoundland and Labrador
[email protected] ABSTRACT This dissertation examines the demographic composition and current working conditions among archaeological practitioners in Canada. Previous research documenting the archaeological population has occurred most readily in the United States and the United Kingdom; by contrast, little is known about the Canadian context. To explore this topic, I executed a mixed-methods research design that gathered longitudinal data pertaining to education and employment in archaeology, administered an online survey to the current archaeological population in Canada, and conducted semi-structured interviews with women currently situated within the discipline. The presentation of a long-term, gendered analysis (binary) of available datasets on the archaeological population revealed that more women are educated in archaeology/anthropology departments but are underrepresented in both academic and CRM workplaces. Using both quantitative and qualitative analyses, these structural data were supplemented and compared with the results yielded through the survey and interviews. While the quantitative analysis of survey data further contextualized these findings and aimed to facilitate an understanding of the dynamics at play in archaeological education and work, the qualitative, thematic analysis of interviews allowed these findings to be explored through lived experiences. By approaching this research through a feminist, intersectional lens, these data were used to attempt to develop relational understandings beyond the male/female dichotomy and explore the social composition of archaeology through other identity-based variables.