International Norms, Moral Psychology, and Neuroscience
Bibliographic record
Abstract
Research on international norms has yet to answer satisfactorily some of our own most important questions about the origins of norms and the conditions under which some norms win out over others. The authors argue that international relations (IR) theorists should engage more with research in moral psychology and neuroscience to advance theories of norm emergence and resonance. This Element first provides an overview of six areas of research in neuroscience and moral psychology that hold particular promise for norms theorists and international relations theory more generally. It next surveys existing literature in IR to see how literature from moral psychology is already being put to use, and then recommends a research agenda for norms researchers engaging with this literature. The authors do not believe that this exchange should be a one-way street, however, and they discuss various ways in which the IR literature on norms may be of interest and of use to moral psychologists, and of use to advocacy communities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".