Approaches to Ethical Decision‐Making: Contrasting Rationality‐Based Models Versus Moral Intuitionism
Bibliographic record
Abstract
ABSTRACT This paper critically examines traditional utilitarian models of ethical decision‐making rooted in rational choice theory. It highlights their limitations as we address ethical dilemmas' complexity, context‐specificity, and nonlinearity. We argue that the foundational axioms of rationality—transitivity, dominance, and invariance—often fail in real‐world ethical contexts. Ethical decisions are rarely dictated by purely rational deliberation; instead, they unfold through intuitive, affective, and culturally influenced judgements. As an alternative to utilitarian and process models, bridging rationalist and phenomenological perspectives, we underscore the need for context‐sensitive, nonlinear approaches to ethics, especially in an era marked by dilemmas in artificial intelligence, climate ethics, and global inequality. We propose that moral intuitionism, which emphasizes intuitive affective judgements drawn from personal and cultural experiences, provides a more realistic framework as an essential tool for understanding the evolving ethical landscape. Intuitionism recognizes ethical behaviour's nonlinear and emergent nature as central to understanding and resolving moral conflicts. Ultimately, the authors invite readers to rethink the dominant rationalist paradigm and consider how intuition, shaped by cultural and emotional contexts, can better navigate the moral challenges of contemporary life.
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.040 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| 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".