Is ethics a Utopia? Yes, when moral distinctions impair the ethical aim
Bibliographic record
Abstract
Ethics and moral philosophy rely heavily on the binary distinction between good and bad. If they are to maintain relevance in a digitally transformed society, the translation of some of their analog content into digital one could be seen as a requirement. A different path is chosen here. We ask: ‘Is ethics a utopia?’ In Niklas Luhmann’s digital theorizing, the answer is ‘Yes’. This is not a final verdict. Some of the pseudo-binary distinctions proposed by philosopher Paul Ricœur in his analog theorizing on ethics and utopia can also contribute to the discussion. The answer then becomes: ‘Yes, when moral distinctions impair the ethical aim’. This impairment is not necessarily fatal. Luhmann does show how binary distinctions such as the code of the moral have a blinding effect because they exclude the third. Ricœur, however, explains how, as an aim, ethics could nevertheless be actualized. Learning is mentioned as recourse by both authors: learning to take into account that the exclusion of the third by binary codes is only an artifice, and learning how to use the resources of both logic and imagination when trying to solve ethical dilemmas. Our approach illustrates how digital and analog theorizing, each in its own way, can enrich the interdisciplinary study of ethics.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.048 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".