Does Saying “Thanks a Lot” Make You Look Less Than? The Magnitude of Gratitude Shapes Perceptions of Relational Hierarchy
Bibliographic record
Abstract
Gratitude expressions provide shared warmth benefits to both the thanker and the helper. Little work has explored how gratitude expressions shift how observers see these individuals on another important dimension: their relative rank. We consider how the magnitude of gratitude expressions shapes observers’ perceptions of the thanker’s and helper’s relative status and power. Seven studies find that thankers who expressed more, compared to less, intense gratitude than expected made their helpers seem relatively higher rank, though this pattern was inconsistent across metrics of gratitude in studies using real-world expressions. These effects emerged across a range of contexts and relationships. Mechanism analyses yielded several null results, while underscoring the role of perceptions of agency. Observers may make their inferences directly, based on intuiting the real association between a thanker’s gratitude and their helper’s power and status. Our findings demonstrate that gratitude signals rank relationships, qualifying current recommendations to express gratitude publicly.
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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 | 0.003 | 0.015 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".