Bibliographic record
Abstract
Mind perception entails ascribing mental capacities to other entities, whereas moral judgment entails labeling entities as good or bad or actions as right or wrong. We suggest that mind perception is the essence of moral judgment. In particular, we suggest that moral judgment is rooted in a cognitive template of two perceived minds-a moral dyad of an intentional agent and a suffering moral patient. Diverse lines of research support dyadic morality. First, perceptions of mind are linked to moral judgments: dimensions of mind perception (agency and experience) map onto moral types (agents and patients), and deficits of mind perception correspond to difficulties with moral judgment. Second, not only are moral judgments sensitive to perceived agency and experience, but all moral transgressions are fundamentally understood as agency plus experienced suffering-that is, interpersonal harm-even ostensibly harmless acts such as purity violations. Third, dyadic morality uniquely accounts for the phenomena of dyadic completion (seeing agents in response to patients, and vice versa), and moral typecasting (characterizing others as either moral agents or moral patients). Discussion also explores how mind perception can unify morality across explanatory levels, how a dyadic template of morality may be developmentally acquired, and future directions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| 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".