Bibliographic record
Abstract
Our understanding of human morality would benefit from an integrated interdisciplinary approach, built on the assumption that human beings are multidimensional unities with real, irreducible, and mutually interdependent spiritual, relational, emotional, rational, and physiological aspects. We could integrate relevant information from neurobiological, psychosocial, and theological perspectives, avoiding unnecessary reductionism and naturalism. This approach is modeled by addressing the particular limited role of disgust in morality. Psychosocial research reveals disgust as a universal emotion that enables evaluation and regulation of certain moral behaviors and is involved in cultural identity. Theologically, many religious traditions, including the Judeo‐Christian, use disgust in conjunction with moral codes designed to preserve purity and communal identity as the people of God. The concept of natural moral law suggests that morality is embodied in human nature. Neurobiology is beginning to trace the neural circuitry involved in disgust and in moral evaluation, suggesting that emotions are a necessary basis for moral judgment and revealing intriguing relationships between disgust, morality, and other aspects of the psyche. Several problems that arise within these disciplines and at their intersections are identified. Extension of the model to other aspects of human morality would further illuminate our understanding of morality without sacrificing its complexity and richness.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".