Gender Pay Gap: A Cross-Sectional Study of the Effect of Workplace Entitlement on Pay Differences
Bibliographic record
Abstract
AimPast empirical studies investigating the possible causes of the gender pay gap have focused on cognitive trait differences between males and females.While several researchers have concluded that personality (or non-cognitive) traits play a role in the pay gap, no definitive lists of personality variables have been discovered to explain the gender pay differentials.We explored whether self-entitlement may result in sex differences in expected salaries. MethodsWe surveyed 413 undergraduate students from an introductory university course studying management to investigate the possible relationship between employee entitlement and expected pay.The survey included two parts of questions asking about participants' employee entitlement and expected pay for different occupations, which reflected potential careers from the management program. ResultsWhile the results showed some sex differences, there were only a few significant relationships between employee entitlement and expected pay. ConclusionAlthough entitlement correlated positively with some of the expected starting salaries, the results do not definitely explain the sex differences in pay as men and women scored higher on certain facets of entitlement.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".