Representation of the Dystopian Nature of Technology in Literature
Bibliographic record
Abstract
The word AI or Artificial Intelligence, booming in this era of digital humanities and technological world, has widely and swiftly advanced in almost all parts of the globe. As everything has two sides of the coin, or rather multiple sides, it is the same with AI, it has its pros and cons too. This paper focuses on the gloomy, adverse and sombre side of the AI and technology as presented in the literature. Many literary works paint the dystopian and apocalyptic image of the same. The paper parallelly examines the novels like Oryx and Crake, Machines Like Me, and Solaris by Margaret Atwood, Ian McEwan, Stanislaw Lem; Canadian, British and, Polish novelists respectively. It also frames analogously, a depiction of AI, technology and the element of humane versus non-humane. The technology has many positive sides but this paper will centralise more on the nightmarish elements of the same by giving literary examples from literature as, literature, from many decades ahead foreseen the Orwellian future of society due to the unsupervised use of AI and technology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".