Which social comparisons influence happiness with unequal pay?
Bibliographic record
Abstract
We examine which social comparisons most affect happiness with pay that is unequally distributed (e.g., salaries and bonuses). We find that ensemble representation-attention to statistical properties of distributions such as their range and mean-makes the proximal extreme (i.e., the maximum or minimum) and distribution mean salient social comparison standards. Happiness with a salary or bonus is more affected by how it compares to the distribution mean and proximal extreme than by exemplar-based properties of the payment, like its comparison to the nearest payment or its distribution rank. This holds for randomly assigned and performance-based payments. Process studies demonstrate that ensemble representations lead people to spontaneously select these statistical properties of pay distributions as comparison standards. Exogenously increasing the salience of less extreme exemplars moderates the influence of the maximum on happiness with pay, but exogenously increasing the salience of the distribution maximum does not. As with other social comparison standards, top-down information moderates their selection. Happiness with a bonus payment is influenced by the largest payment made to others who solve the same math problems, for instance, but not by the largest payment made to others who solve different verbal problems. Our findings yield theoretical and practical insights about which members of groups are selected as social comparison standards, effects of relative income on happiness, and the attentional processes involved in ensemble representation. (PsycInfo Database Record (c) 2021 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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".