Transnational Canadas: Cultural production and the construction of literary communities in English Canada
Bibliographic record
Abstract
My dissertation, "Transnational Canadas: Cultural Production and the Construction of Literary Communities in English Canada," contributes to ongoing debates within the fields of Canadian literature and globalization studies by examining the shifts in political thought underlying literary writing produced in Canada since the centennial. How has literature changed since the period of cultural nationalism that produced writers such as Margaret Atwood and Dennis Lee? What agendas underlie their works and those of younger generations of writers in Canada? In addressing these questions, I argue that the rise of a globalizing mindset has increasingly pushed writers to look towards transnational cultural flows for answers about how to write ethically minded literatures. I examine the theoretical field of transnationalism in depth, looking at its genesis in the uneasy confluence of Marxist, poststructural, Indigenous, and postcolonial studies. I focus on the later writings of Jacques Derrida, those of Fredric Jameson, and Gayatri Spivak in order to generate my understanding of the confluence of poststructural, Marxist, and postcolonial thought, and focus on Indigenous scholars Taiaiake Alfred and Craig Womack in order to situate these earlier discourses within a North American context. These theorists all inflect my readings of transnational studies, especially the of writings of Michael Hardt and Antonio Negri. I use these theoretical lenses in order to examine writers in Canada from Leonard Cohen to Dionne Brand. Through reading such texts as Jeannette Armstrong's Slash and Joy Kogawa's Obasan, I delineate the shifts in Canadian literature from cultural nationalism through the debates of the culture wars and identity politics up to the present day. I pursue this inquiry as a means of demonstrating that the changes in writing in Canada are not merely related to overt political reasoning. Rather, I argue that these changes are also epistemological, related to shifting concepts of subjectivity in Western, postcolonial, and Indigenous thought, in which the self becomes increasingly open, questioning the oppositional modes of conducting politics that the consolidation of a Canadian identity around the centennial seems to invite.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.035 | 0.026 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".