Problem-Solving Skills of the U.S. Workforce and Preparedness for Job Automation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Automation and advanced technologies have increased the need for a better understanding of the skills necessary to have a globally competitive workforce. This study used data from the Program for the International Assessment of Adult Competencies to compare problem-solving skills in technology-rich environments among adults in South Korea, Germany, Singapore, Japan, Canada, Estonia, the United Kingdom, the United States, and Australia. Overall, the United States had the lowest scores among all countries, and in all countries scores declined with age. The United States had higher proportions of survey participants in the lowest skill category and lower proportions in the top-skill categories. The results of this study suggest changes in the U.S. educational and lifelong learning systems, and policies may be necessary to ensure all adults have the necessary skills in a competitive workforce.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it