Artificial Life and the Loss of Biodiversity: Ecological Consequences of Genetic Experiments in Oryx and Crake
Bibliographic record
Abstract
Literature serves as a social representation. It represents the people, their inventions, and their environmental consequences. The Canadian writer Margaret Atwood’s Oryx and Crake is a dystopian novel that explores a world where corporate greed and scientific pride have collapsed the environment and replaced natural biodiversity with genetically modified beings. Genetic engineering has become both a wonder and a threat, raising intense ethical and existential questions in the contemporary world. The narrative moves along with the character Snowman, possibly the last human survivor in the apocalyptic world, who reflects on the consequences of experimentation on genetic modifications, especially by his friend Crake. It also explores the diverse genetic modifications the profit-driven corporate society has crafted, devoid of moral constraints. The novel through its hubris and ethical blind spots, critiques the human tendency to play the role of God by controlling nature without considering its consequences. This paper examines Atwood’s cautionary depiction of genetic engineering and highlights the novel’s warning about the need for ethical boundaries and regulatory supervision in scientific endeavors through an ecocritical perspective.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.059 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".