Does higher education make a difference? The influence of educational attainment on women's and men's nonstandard employment outcomes
Bibliographic record
Abstract
Some studies suggest that women and the less educated are more likely to be employed in nonstandard work. However, conflicting evidence has indicated that temporary, part-time, and nonstandard self-employment has diffused across different social groups and levels of education. Using pooled data from the 1997-2018 Canadian Labour Force Surveys, this study explores the changing relationship between higher education, gender, and employment outcomes. Taking a multiple logistic regression approach, this study accomplishes three objectives: (1) to examine the relationship between gender and education among different forms of nonstandard employment; (2) to investigate the changes of different forms of nonstandard employment between 1997 to 2018; (3) to analyze the association between men's and women's education and their likelihood of different forms of nonstandard employment are explored. The findings suggest the feminization of employment norms, in which men and women have had some convergence in certain types of nonstandard employment. This result holds across most levels of education, but is more pronounced for women and men with higher levels of education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".