The Social and Ethical Acceptability of NBICs for Purposes of Human Enhancement: Why Does the Debate Remain Mired in Impasse?
Bibliographic record
Abstract
The emergence and development of convergent technologies for the purpose of improving human performance, including nanotechnology, biotechnology, information sciences, and cognitive science (NBICs), open up new horizons in the debates and moral arguments that must be engaged by philosophers who hope to take seriously the question of the ethical and social acceptability of these technologies. This article advances an analysis of the factors that contribute to confusion and discord on the topic, in order to help in understanding why arguments that form a part of the debate between transhumanism and humanism result in a philosophical and ethical impasse: 1. The lack of clarity that emerges from the fact that any given argument deployed (arguments based on nature and human nature, dignity, the good life) can serve as the basis for both the positive and the negative evaluation of NBICs. 2. The impossibility of providing these arguments with foundations that will enable others to deem them acceptable. 3. The difficulty of applying these same arguments to a specific situation. 4. The ineffectiveness of moral argument in a democratic society. The present effort at communication about the difficulties of the argumentation process is intended as a necessary first step towards developing an interdisciplinary response to those difficulties.
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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 | 0.077 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.094 |
| Scholarly communication | 0.023 | 0.030 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.028 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".