The Environmental Activist in Contemporary U.S. and Canadian Novels
Bibliographic record
Abstract
This thesis examines the representation of environmental activists in U.S. and Canadian novels from 1998-2020, surveying works written by authors including Margaret Atwood, T.C. Boyle, Octavia Butler, Michael Christie, Louise Erdrich, Jonathan Franzen, Barbara Kingsolver, Lydia Millet, Tommy Orange, Ruth Ozeki, Richard Powers, Kim Stanley Robinson, Tanya Tagaq, and Jeff VanderMeer. Despite a growing body of environmental literary criticism, also known as ecocriticism, portrayals of activists in the novel have received scant critical attention. Examining the portrayal of environmental activists in narrative fiction is crucial for a fuller understanding of how novelists address climate change and other environmental issues. Drawing on empirical, affective, and feminist ecocriticism, the thesis analyses depictions of fictional activists amid the increasing incidence of writers self-identifying their work as activism and asks which approaches to character support or undermine writers’ interest in motivating reader action on climate issues. One of the key representational challenges facing writers is the prevalence with which fictional activists are commonly depicted as fanatical, ineffective, or motivated by suspect causes, underscoring the anxiety and denial surrounding climate change in the Canadian and U.S. societies. The thesis traces the evolution of the archetypal literary environmental activist, descended from Edward Abbey, from something that can bluntly be described as negative to positive, simplistic to complex, impulsive to strategic. I read activists in several different contexts, with chapters devoted to gender and environmental justice, religion, biocentrism, Indigenous activism, and optimistic climate fiction. Throughout, the thesis considers the novel’s possibilities for effectively representing responses to climate change. Ultimately, I will argue that much in the way that activist representations in novels are influenced by the social and political climates in which they are created, these characterisations may in turn influence perceptions of and responses to the environment, climate change, and environmentalism beyond the page.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.041 | 0.019 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".