Rehashing the moral-conventional distinction: perceived harm marks the border
Bibliographic record
Abstract
Turiel and colleagues divided norms into two kinds: Moral norms and conventional norms. Moral norms are universal, concerned with welfare, justice, fairness, equality and/or rights, and rule/authority independent. Conventional norms are local, rule/authority dependent, and concerned with maintaining social coordination, preserving tradition, and avoiding punishment. This account has been challenged, and the existence of a crisp distinction remains debatable. In this paper, I defend a version of the moral/conventional distinction on the basis that people generally judge norms concerned with welfare violations as moral, and that they distinguish between the morality of the action and that of the agent. I argue that we can make sense of the mixed data because people differ in how they perceive harm. Harm perception, rather than “objective” harm, is the relevant variable in knowing how people judge norms. To illustrate this, I explore why people perceive norms differently and how the moral/conventional distinction is reflected not only between cultures, but within them too. This should prompt us to reimagine the moral/conventional distinction to account for this diversity in perceptions. Doing away with the distinction risks misrepresenting both inter- and intra-cultural diversity in perceptions of harm.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.028 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| 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".