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Record W2322713270 · doi:10.1386/eme.14.3-4.275_1

The body electric in the age of virtual reality and transhumanism: Forces changing the West’s notions of self, identity and humanness

2015· article· en· W2322713270 on OpenAlexaff
Robert B. Scott

Bibliographic record

VenueExplorations in Media Ecology · 2015
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTranshumanismIdentity (music)SociologyWearable computerHuman bodyEnvironmental ethicsAestheticsComputer sciencePhilosophyArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract This article begins by exploring the relationships connecting Walt Whitman’s ‘body electric’, Marshall McLuhan’s ‘discarnate man [sic]’ and Neil Postman’s ‘citizen of technopoly’. It then examines the disruptive effects that electric technologies have had on western culture’s notions of ‘self’, ‘identity’ and particularly ‘the body’, especially the development of Virtual Reality systems, social networking and transhuman technologies over the past three decades. A survey of new wearable and immersive devices, advances in artificial intelligence, and invasive biotechnical procedures reveals increasingly questionable attempts to replace the human body by machines. These endeavours are supported, notably, by some of the world’s wealthiest investors in artificial intelligence and biotechnology, statesmen, media barons and members of the military – the forces of technopoly. The article concludes that, despite the efforts to replicate, improve or even replace it, the body is essential to our humanness.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.036
Scholarly communication0.0080.010
Open science0.0000.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.056
GPT teacher head0.316
Teacher spread0.260 · 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 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
Published2015
Admission routes1
Has abstractyes

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