Bibliographic record
Abstract
One of the most widely accepted tenets in postmetaphysical normative ethics is the principle of dialogue as a foundational authority. Conceptually, the dialogical model is valuable, in that it allows a binding yet mutable underpinning of moral discourse. However, dialogue has its limits. The main drawback lies in the fact that deliberations can be very lengthy, perhaps even infinite. In other words, deliberation does not always lend itself to action. From the vantage point of applied ethics, in this case, bioethics and forensic ethics, this is not a minor shortcoming since these disciplines are concerned with situations involving some sort of urgency. Aporia is not an option in most cases. It is thus pressing to consider the moment of decision inherent to moral judgement and action that puts an end to dialogue. From the realm of abstract norm justification, one must move on to the contextualist discourse of application. To be rational, however, decision must avoid authoritativeness or any type of arbitrariness. What, then, could serve as a reasonable criteria for the rational application of norms? What aspects should be envisaged for a sound reflective decision? This paper will attempt to sketch some answers that could contribute to the question of the necessary contextual application of accepted norms by paying heed to, systemizing and sometimes finding fault in the arguments given by judges who had to settle cases touching sensitive moral issues.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.058 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".