Nanotechnologies and Ethical Argumentation: A Philosophical Stalemate?
Bibliographic record
Abstract
When philosophers participate in the interdisciplinary ethical, environmental, economic, legal, and social analysis of nanotechnologies, what is their specific contribution? At first glance, the contribution of philosophy appears to be a clarification of the various moral and ethical arguments that are commonly presented in philosophical discussion. But if this is the only contribution of philosophy, then it can offer no more than a stalemate position, in which each moral and ethical argument nullifies all the others. To provide an alternative, we must analyze the reasons behind the prevailing individual and cultural relativism in ethics. The epistemological investigation of this stalemate position will guide us to the core problem of the relation between theory and action (“Part 1: From a conceptual to a speech act analysis of moral arguments”). The stalemate can be overcome from a pragmatic philosophical standpoint, which combines epistemology, philosophy of language—that is, the philosophy of speech acts—and practical reasoning—that is, reasoning about decision-making (“Part 2: Moral argumentation from a pragmatist perspective”). From this philosophical standpoint, it will be possible to show how philosophy can accompany and support the development of nanotechnologies (“Part 3: Philosophy and the evaluation of the development of nanotechnologies”).
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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 | 0.067 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.011 | 0.113 |
| Scholarly communication | 0.025 | 0.058 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.021 | 0.028 |
| 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".