The Problem of Purity in Moral Psychology
Bibliographic record
Abstract
Academic Abstract The idea of “purity” transformed moral psychology. Here, we provide the first systematic review of this concept. Although often discussed as one construct, we reveal ~9 understandings of purity, ranging from respecting God to not eating gross things. This striking heterogeneity arises because purity—unlike other moral constructs—is not understood by what it is but what it isn’t: obvious interpersonal harm. This poses many problems for moral psychology and explains why purity lacks convergent and divergent validity and why purity is confounded with politics, religion, weirdness, and perceived harm. Because purity is not a coherent construct, it cannot be a distinct basis of moral judgment or specially tied to disgust. Rather than a specific moral domain, purity is best understood as a loose set of themes in moral rhetoric. These themes are scaffolded on cultural understandings of harm—the broad, pluralistic harm outlined by the Theory of Dyadic Morality. Public Abstract People are fascinated by morality—how do people make moral judgments and why do liberals and conservatives seem to frequently disagree? “Purity” is one moral concept often discussed when talking about morality—it has been suggested to capture moral differences across politics and to demonstrate the evolutionary roots of morality, especially the role of disgust in moral judgment. However, despite the many books and articles that mention purity, there is no systematic analysis of purity. Here, we review all existing academic articles focused on purity in morality. We find that purity is an especially messy concept that lacks scientific validity. Because it is so poorly defined and inconsistently measured, it should not be invoked to explain our moral minds or political differences.
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 | 0.021 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| 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".