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Record W4413207189 · doi:10.3390/rel16081028

Transhumanism, Religion, and Techno-Idolatry: A Derridean Response to Tirosh-Samuelson

2025· article· en· W4413207189 on OpenAlexafffund
Michael G. Sherbert

Bibliographic record

VenueReligions · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsQueen's University
FundersCanada First Research Excellence Fund
KeywordsTranshumanismReinterpretationIdolatryMeaning (existential)EpistemologyPhilosophyWorshipFunction (biology)AestheticsTheologyBiology

Abstract

fetched live from OpenAlex

This paper critiques Hava Tirosh-Samuelson’s view of transhumanism as techno-idolatry by applying Derrida’s notion of the unconditional “to-come” and the generalized fetish. While acknowledging Tirosh-Samuelson’s stance that fetishes should not be reduced to idols, I argue that she fails to extend this understanding to transhumanism, instead depicting its fetishes as fixed idols. Drawing on Derrida’s notion of the generalized fetish, I argue that religious objects in Judaism (like the shofar or tefillin) function not as objects of worship but as material mediators of divine relation—tangible signs that carry symbolic, spiritual, and covenantal meaning while gesturing toward the divine without claiming to contain or represent it. Similarly, in transhumanism, brain-computer interfaces and AI act as fetishes that extend human capability and potential while remaining open to future reinterpretation. These fetishes, reflecting Derrida’s idea of the unconditional “to-come,” resist closure and allow for ongoing change and reinterpretation. By reducing transhumanism to mere idolatry, Tirosh-Samuelson overlooks how technological fetishes function as dynamic supplements, open to future possibilities and ongoing reinterpretation, which can be both beneficial and harmful to humanity now and in the future.

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)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.330
Teacher spread0.300 · 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 designBench or experimental
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
Published2025
Admission routes2
Has abstractyes

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