American Gratitude-plus-Pride Differentially Predict Benevolent and Activism Support Across Political Ideology
Bibliographic record
Abstract
Given rising anti-immigrant sentiment in the West, it is increasingly important to further investigate which psychological factors may facilitate helping towards immigrants. In two studies with White Americans (Total N = 1,139) we investigated whether (and when) two positive emotions — group-based gratitude and group-based pride — influence benevolent and activism support toward immigrants in the U.S. Although previous research on individual-level gratitude and pride suggests that they are distinct emotions, at the group-level, we find that American gratitude and pride clustered together and were statistically indistinguishable. Importantly, this combination of American gratitude-plus-pride had differential effects on helping towards immigrants, depending on two key factors: political orientation of the perceiver, and the type of support rendered. Among conservatives and moderates, American gratitude-plus-pride was predictive of increased benevolent support. However among liberals, it was related to decreased activism support. Our findings suggest that the impact of group-based gratitude and pride on helping towards immigrants is complex, and contingent upon preexisting dispositions.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".