Revisiting the gender job satisfaction paradox: The roots seem to run deep
Bibliographic record
Abstract
Abstract Using the Canadian General Social Survey of 2016, a large nationally representative dataset, this article estimates the gender job satisfaction gap. This unique dataset allows to control for nearly all the variables previously suggested to explain the well‐documented higher job satisfaction of women, such as task characteristics, job flexibility and the quality of social relations at work. Accounting for all these variables, the gender gap in favour of women remains in the aggregate sample. But, when the sample is partitioned by age and educational attainment, among the youngest and the most educated workers, the gender job satisfaction gap is found to be statistically insignificant. In addition, these data allow for the inclusion of subjectively experienced intrinsic job rewards, such as sense of pride and accomplishment from work, rarely available in social surveys. With the inclusion of these variables, the gender job satisfaction gap loses its statistical significance across all age groups and educational attainment levels, except for those with a graduate university degree. For the latter group, the gender job satisfaction gap is actually reversed in favour of men. Consistently, an Oaxaca–Blinder decomposition of the gender job satisfaction gap indicates that the ‘explained’ component of the gender gap substantially increases with the inclusion of intrinsic job rewards. The implications are discussed.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".