Would the Convergence of Nanotechnology, Biotechnology, Information Technology and Cognitive Science Be a Springboard for Transhumanism and Posthumanism?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it