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Record W7162468573 · doi:10.32628/ijsrst24116523

Artificial Life and the Loss of Biodiversity: Ecological Consequences of Genetic Experiments in Oryx and Crake

2024· article· W7162468573 on OpenAlexaboutno aff
Ms. A. Sri Sivabagya

Bibliographic record

VenueInternational Journal of Scientific Research in Science and Technology · 2024
Typearticle
Language
FieldArts and Humanities
TopicUtopian, Dystopian, and Speculative Fiction
Canadian institutionsnot available
Fundersnot available
KeywordsHubrisDystopiaOryxWonderDepictionPrideNatural (archaeology)EcocriticismPosthumanism

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.041
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.131
GPT teacher head0.385
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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