How do I Show Gratitude to God? Paying it Back versus Paying it Forward
Bibliographic record
Abstract
Religion has a long history of inspiring gratitude in its adherents. Despite the consistent link between religion and gratitude, only a few studies have begun to provide a deeper understanding of gratitude specifically toward God. Using qualitative (Study 1), cross-sectional (Study 2), daily experience (Study 3), and experimental data (Study 4), I sought to close the gap by (a) attaining a better sense of the general importance of gratitude to God among those are religiously affiliated, (b) psychologically disentangling motivation to pay gratitude back versus forward, and (c) evaluating how people show gratitude to God, (d) all in comparison to interhuman gratitude. I found several consistent findings across studies. Those with a religious affiliation consistently reported high endorsement of gratitude to God. They strongly endorsed statements about being motivated to pay gratitude to God back and forward, and the motivation to pay gratitude to God forward was consistently higher than motivation to pay gratitude forward from any other interhuman target (e.g., close other, authority figure). Gratitude to God consistently predicted engagement in specific religious behaviors, but also more general character development and prosocial behavior. The motivation to pay gratitude to God forward more consistently mediated these effects than the motivation to pay gratitude to God back. In most cases, gratitude to God predicted these outcomes above and beyond interhuman gratitude. These results highlight the importance of gratitude to God in the lives of those with a religious affiliation, the differences between gratitude experienced toward God in comparison to interhuman gratitude, and the potential for gratitude to God to motivate religious behavior, character development, and prosocial behavior.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| 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".