Biting the hand that feeds: A status-based model of when and why receiving help motivates social undermining.
Bibliographic record
Abstract
Social exchange theory suggests that after receiving help, people reciprocate by helping the original help giver. However, we propose that help recipients may respond negatively and harm the help giver when they perceive helping as a status threat and experience envy. Integrating the helping as status relations framework and the social functional perspective of envy, we examine when and why receiving help may prompt help recipients to undermine help givers. Across four studies, we find progressive support for our results, which show that when individuals receive task-related help from help givers who are perceived to be more, rather than less, competent than them, they experience greater status threat and envy. As help recipients experience envy toward help givers, they are likely to undermine help givers, and this positive relationship becomes stronger for help recipients who have higher status striving motivation. Our findings underscore the status dynamics implicated in helping interactions by highlighting that help recipients, especially those with higher status striving motivation, may paradoxically undermine help givers when they perceive status threat from and feel envious of help givers, as a result of receiving help from more competent help givers. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".