A Global Study of Pay Preferences and Employee Characteristics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Companies are managing more diverse work forces, and pay systems must be designed to attract, retain and motivate employees who may have very different pay preferences from employees of even a decade ago. This study examines how employee characteristics (i.e., gender, age, education, work experience, annual pay and number of dependents) are related to pay preferences. We found that older respondents with more education and more dependents had a stronger preference for variable pay than did respondents who were younger, less educated and had fewer dependents. Older respondents and those with higher pay preferred less pay transparency than did younger and lower paid respondents. Pay differences based on capability were preferred by better educated employees. When controlling for the other demographic characteristic, we found significant differences among nationalities for all four measures of pay preferences, that is, pay differences, pay variability, bonus plans and pay transparency.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it