Quacking in the Anthropocene: Ecocide and Climate Change in Kate Beaton’s Ducks: Two Years in the Oil Sands
Bibliographic record
Abstract
In recent years, the graphic narrative medium has emerged as a potent site for interrogating the complexities of human-nature relations in the context of the Anthropocene epoch. With its unique fusion of visual and textual elements, this medium, transcends the boundaries of traditional storytelling, seamlessly merging artistry with activisms to illuminate the pressing societal concerns of our times. This research paper delves deep into the ecological dimensions of Kate Beaton’s graphic memoir1 Ducks: Two Years in the Oil Sands situated at the confluence of climate change discourse, eco-horror and the emerging discourse on ecocide, an intentional or systematic destruction of ecosystems driven by extractivist economies. Beaton’s portrayal of the Alberta oil-sands as a grotesque, polluted wasteland epitomizes the genre’s capacity to evoke a visceral response to the climate crisis. Her depiction of oil sands workers’ precarious conditions and the pervasive environmental destruction they encounter again reminds us of the discourse on the Anthropocene, highlighting the intersectionality of labour, exploitation, and environmental harm. As Rob Nixon posits in Slow Violence and the Environmentalism of the Poor, the Anthropocene is characterized by “slow violence” and “…it is those people lacking resources who are the principal casualties…” (4). This paper argues that Ducks transcends mere documentation of environmental and human injustices by interrogating the systemic structures that sustain ecocide and ecological amnesia2. The memoir situates individual and collective trauma within broader Anthropocene narratives, challenging readers to reckon with the ethical implications of extractivism and the commodification of nature. While celebrating the aesthetic and thematic innovations of Beaton’s work, this research also interrogates potential limitations in its narrative strategies, addressing the interplay between personal testimony and structural critique. By critically analysing the ecological, ethical and political dimensions of Ducks within the framework of ecocide and eco-horror, this paper contributes to the growing body of scholarship on graphic narratives as potent tools for eco-critical inquiry into the Anthropocene.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.014 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".