“What’ve I done to deserve this?” The role of deservingness in reactions to being an upward comparison target
Bibliographic record
Abstract
Outperforming others may be an ambivalent experience, simultaneously evoking pride and discomfort. Two experiments examined the role of deservingness in reactions to being an upward comparison target. Study 1 took place online and experimentally manipulated deservingness by modifying a self-report measure of Sensitivity about Being the Target of a Threatening Upward Comparison (STTUC). Participants predicted more distress and less positive affect under conditions of undeserved (vs. deserved) success; several individual difference variables moderated these effects. Study 2 systematically varied a confederate’s effort to manipulate the perceived deservingness of an outperformed person. Participants were especially likely to downplay their score in the presence of a confederate who appeared to work hard on a task but nevertheless performed poorly. Collectively, findings suggest that people respond most strongly to STTUC when a mismatch exists between deservingness and outcomes.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".