AN INTERNATIONAL COMPARISON OF INCOME DISPARITIES BY PROBLEM-SOLVING SKILLS, GENDER, AND AGE
Bibliographic record
Abstract
Abstract Income disparities by gender have been a persistent problem in economically-developed countries for decades, with income gaps often widening over the adult life course. We use data from the 2012 Program for the International Assessment of Adult Competencies (PIAAC) to examine relationships among problem solving skills in technology-rich environments (PSTRE), income, sex, and age in Australia, Canada, England/Northern Ireland and the United States. Women age 35 to 44 in the middle-to-high (i.e., 50th - 75th percentile) income group had significantly higher PSTRE scores than their male counterparts in Australia and Canada. For the same income group, women ages 55 to 65 had significantly higher. PSTRE scores than men in Canada and England/Northern Ireland. These results suggest that women with similar skills lagged their male counterparts in income in specific sub-populations in specific countries. We provide possible explanations for these differences and conclude with implications for policy and practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".