When What You Want is What You Get: Pay Dispersion and Communal Sharing Preference
Bibliographic record
Abstract
The question of whether pay structures should be compressed or dispersed remains unanswered. We argue that pay dispersion can yield uncertainty regarding others' intentions and behaviors; as a result, individuals take a greater risk trusting their group members as pay spreads widen. Accordingly, we explore the conditions under which individuals are more willing to take this risk by viewing their group members as trustworthy even when pay is dispersed. Specifically, preferences for how relationships and resources should be structured in groups should help to determine when pay dispersion relates to trustworthiness perceptions. We hypothesise that the cross‐level interaction between preferences for communal sharing (Level 1)—that is, the extent to which individuals prefer communal, egalitarian structures in their groups—and pay dispersion (Level 2) is associated with trust perceptions. Data drawn from a sample of university professors support our hypothesised cross‐level interaction, and show that when pay dispersion is greater, individuals perceive their group members as more trustworthy only when they have weak preferences for communal sharing. Our results signify the importance of individual attributes to understanding pay dispersion's effects, and show that trust is fostered when preferences and pay conditions are aligned.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".