Bibliographic record
Abstract
Moral values guide consequential attitudes and actions. Here, we report evidence of seasonal variation in Americans' endorsement of some-but not all-moral values. Studies 1 and 2 examined a decade of data from the United States (total N = 232,975) and produced consistent evidence of a biannual seasonal cycle in values pertaining to loyalty, authority, and purity ("binding" moral values)-with strongest endorsement in spring and autumn and weakest endorsement in summer and winter-but not in values pertaining to care and fairness ("individualizing" moral values). Study 2 also provided some evidence that the summer decrease, but not the winter decrease, in binding moral value endorsement was stronger in regions with greater seasonal extremity. Analyses on an additional year of US data (study 3; n = 24,199) provided further replication and showed that this biannual seasonal cycle cannot be easily dismissed as a sampling artifact. Study 4 provided a partial explanation for the biannual seasonal cycle in Americans' endorsement of binding moral values by showing that it was predicted by an analogous seasonal cycle in Americans' experience of anxiety. Study 5 tested the generalizability of the primary findings and found similar seasonal cycles in endorsement of binding moral values in Canada and Australia (but not in the United Kingdom). Collectively, results from these five studies provide evidence that moral values change with the seasons, with intriguing implications for additional outcomes that can be affected by those values (e.g., intergroup prejudices, political attitudes, legal judgments).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".