Post-Truth Ecologies and Manufactured Realities: The Collapse of Knowledge, Media Myths, and Environmental Justice in Margaret Atwood’s Oryx and Crake
Bibliographic record
Abstract
Within the contemporary discourse, where truth is debatable and environmental collapse is excruciatingly institutionalized, Margaret Atwood's Oryx and Crake (2003) accentuates a narrative that interrogates the mitigation of moral and ecological failures of post-truth culture. The tragedies that characterize the twenty-first century, when facts, social duty, and ecological awareness are subservient to business, are all irresistibly immersed in Atwood's speculative work. These include genetic trickery, corporate supremacy, and the monetization of life. This research analyzes Oryx and Crake within the discourse of post-truth and environmental justice in Canada. It positions Atwood's novel as a source of work that discloses how neoliberal capitalism, corporate media, and technology work together to corrode the very notion of truth. Her perspective of dystopia reflects not only a catastrophic environmental disaster along the spectrum of the entire world but also an especially Canadian anxiety about resource exploitation and colonial histories, along with an erasure of Indigenous ecological epistemologies. In Atwood's post-apocalyptic world, environmental degradation reflects the collapse of meaning itself and the end of nature becomes indiscernible as well as indistinguishable from the end of truth. By analyzing myth-media-ecological ethics intersections, this research investigates how Atwood modify environmental degradation as a metaphor for epistemic decay. Through the lens of Indigenous Environmental Thought and post-truth studies, this paper argues that Oryx and Crake initiates a critique of settler narratives of mastery and progress. Atwood's fiction thus functions as both ecological warning and epistemological restoration. It demands a reenvisioning of justice in which truth and the environment are not oxymoronic but they are collectively sustaining and preserving the modes of reality. This research paper demonstrates that how Oryx and Crake illustrates the entanglement and enmeshment of post-truth discourses, ecological crash, and Indigenous environmental justice and what Atwood's speculative vision accentuates about the moral and epistemological failures of the present day Canadian and global post-truth era.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.065 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".