Transhumanism, Religion, and Techno-Idolatry: A Derridean Response to Tirosh-Samuelson
Bibliographic record
Abstract
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.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".