Gender and Perceived Income Entitlement Among Full-Time Workers: Analyses for Canadian National Samples, 1984 and 1994
Bibliographic record
Abstract
In 2 studies, gender differences in perceived income entitlement through the use of survey data from 2 national samples of full-time workers interviewed in 1984 and 1994 were investigated. We examined whether, and how, views on income entitlement of women and men differ when they are asked about their earnings from real full-time jobs. As expected from the experimental literature, there are gender differences in perceived income entitlements before controls for work characteristics and social background characteristics for both samples and time periods; women felt deserving of less than men did. These differences persist even after multivariate controls for the effects of education, age, time in the career, and 3 characteristics of the type of job on which income entitlement views are based. However, significant gender differences in perceived income entitlement do not obtain with added controls for last year's income in the 1984 data. They are much reduced, but still significant in the 1994 data. Both women and men often felt that they deserve somewhat more than they actually earned. In 1984, there was no difference in the proportions of women and men who felt they deserved more income than they earned, but in 1994 proportionally more women than men felt deserving of additional pay. The differences in results across the 2 studies may be due either to changes over time or to small differences in the procedures of the studies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".