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Record W2067315759 · doi:10.1558/imre.v17i4.443

Turning into Gods

2014· article· en· W2067315759 on OpenAlexaff
Olivier Masson

Bibliographic record

VenueImplicit Religion · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsTranshumanismNarrativePhilosophyAestheticsEpistemologyLiteratureFace (sociological concept)UtopiaHistoryArtArt history

Abstract

fetched live from OpenAlex

Transhumanism has been a part of modern culture since the early years of the twentieth century. Since Julian Huxley, (brother of the famous writer Aldous Huxley), first used the term in 1957 to describe what he called a “new belief ” in the capability of the human species to “transcend itself,” transhumanism has been going through a continuous institutionalization process. After spending the second half of the twentieth century as a major leitmotiv of science fiction, since the dawn of the new millennium a large number of texts dealing with the creation of a new human species have been published as non-fiction: thus, what was considered a few years ago to be genuine science fiction themes, are now presented as non fiction. However much this crossover from fictional to non-fictional may have changed the face of transhumanism, it has nevertheless left intact its narrative dimension. In this article we argue that this narrative is what gives transhumanism its implicit religious dimension.

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)0.007
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 score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.034
Scholarly communication0.0060.009
Open science0.0010.006
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0120.003

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 head0.035
GPT teacher head0.335
Teacher spread0.299 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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