Personal relative deprivation negatively predicts engagement in group decision-making
Bibliographic record
Abstract
Inequality has been linked with numerous negative consequences at the societal and individual levels. The detrimental effects of inequality are proposed to operate at the individual level through social comparisons, where perceived unfair disadvantage leads to the experience of personal relative deprivation (i.e., subjective feelings of anger and resentment), which in turn causes psychosocial stress. To date, little empirical work has investigated how individual differences in personal relative deprivation influences group dynamics. In a simulated high-pressure hypothetical scenario, first-year business students (n = 150) in groups of four to six were tasked to reach a consensus decision despite being assigned roles with competing interests, then they individually completed a survey. Greater feelings of personal relative deprivation were associated with reduced group engagement. Personal relative deprivation explained 9% of variance in group exercise engagement over and above demographic and situation-related variables (e.g., stress, perceived competition, role fit), and the overall regression model accounted for 58% of total variance in group. These findings suggest that such negative socioemotional comparison reactions as personal relative deprivation have important implications for group-based decision making and small group dynamics.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".