DISPARITIES IN HUMAN CAPITAL INVESTMENT OVER THE GENDERED LIFE COURSE: AN INTERNATIONAL COMPARISON
Bibliographic record
Abstract
Abstract Income disparities by gender are a persistent problem throughout the world. These disparities place women at risk for economic insecurity both while working and in retirement. Education and continued skill upgrading are key to reducing income disparities, but it is well documented that both older men and women are less likely to participate in adult education and training (AET) than their younger counterparts. In this symposium we present gender and age-based differences in AET in Australia, Canada, England/Northern Ireland and the United States. Also, given the increasing use of technology, technology-related problem-solving skills are compared across these four nations. In addition, we discuss current, and potentially new, country level policies and practices that facilitate the provision of AET over the second half of the life course. Yamashita and colleagues use data from the Program for the International Assessment of Adult Competencies (PIAAC) to provide an overview of AET participation, income, and technology-related problem-solving skills by sex and age groups in the four countries. Vickerstaff and van der Horst use data from five different organizations in the United Kingdom to examine attitudes of older workers about participation in training and the extent to which these attitudes result from self-imposed ageism. Taylor presents survey data from Australia that analyzes types of training women are undertaking, factors associated with participation in training, and the importance of employer support. Finally, Harrington and Cummins use PIAAC data to analyze age variations in AET participation and gender differences in employer sponsored training in Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".