Would the Convergence of Nanotechnology, Biotechnology, Information Technology and Cognitive Science Be a Springboard for Transhumanism and Posthumanism?
Bibliographic record
Abstract
Nanotechnologies, biotechnologies, information technologies and cognitive sciences (NBIC) have gradually gained traction in the United States of America (USA), subsequently expanding to Europe, and are now proliferating worldwide. Scientists are trying with more success to remove the causes of death by “repairing” humans, or even by “increasing” their physical and cognitive capacities. NBICs not only can help researchers promote “one health” by improving environmental conditions, human and animal health, but also, they can lead humanity towards transhumanism through eugenics. Thanks to the principle of totality, the intentional modification of the human body for therapeutic purposes through surgery has always been seen as a source of medical progress. But how far can the living human body be modified at will? Gilbert Hottois and Jean-François Mattei have deciphered transhumanism to question its alleged “humanism” and study its impact on our humanity. Today, science has gone further thanks to the possibilities offered by converging NBIC technologies and especially with the advent of human genome editing! The objective of this article is to highlight the hopes and fears of Homo sapiens following the applications of NBICs, and to propose ethical reflections on the invading transhumanist and posthumanist doctrines that tend to become spiritual movements, even religions. A summary study, based on a scientific bibliography, linked to NBICs and including ethical aspects, will present the ethical issues of the convergence of nanotechnologies, biotechnologies, information technologies and cognitive sciences, which could become a springboard for transhumanism and posthumanism.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".