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Record W3217796684 · doi:10.5206/tba.v2i1.10737

Technological Dominance, "Discarnate Men," and the Vestiges of Humanity in William Gibson's Neuromancer and Philip K. Dick's Do Androids Dream of Electric Sheep?

2020· article· en· W3217796684 on OpenAlexaffvenue
Robert Sparrow-Downes

Bibliographic record

Venuetba Journal of Art Media and Visual Culture · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicUtopian, Dystopian, and Speculative Fiction
Canadian institutionsYork University
Fundersnot available
KeywordsHumanityDreamDystopiaDominance (genetics)ArtArt historyTranshumanismPhilosophySociologyLiteraturePsychologyEnvironmental ethicsTheology

Abstract

fetched live from OpenAlex

In their respective texts, William Gibson and Philip K. Dick both envision the place of humanity in a world dominated by technology, and both perceive and illuminate the dystopian possibilities that may result from the exponential development of technology. By invoking Marshall McLuhan’s observations regarding technological dominance and the resulting “discarnate man,” this essay will aim to show how the numbed interiority of the characters depicted in Neuromancer and Do Androids Dream? is the result of their interactions with technology which has massaged them into new forms of being. Furthermore, this essay will show how, by using AIs and replicants as doubles (or doppelgangers) of the human characters, both novels depart from conventions of science fiction by turning the focus away from the non-human, and back towards human emotion, touch, and empathetic response.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0070.050
Scholarly communication0.0110.007
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.237
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2020
Admission routes2
Has abstractyes

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