Challenges to Engage Low-Skilled Adults in Education and Training: An International Perspective
Bibliographic record
Abstract
Abstract The demand for adult education and training (AET) opportunities throughout the life course is substantial as labor markets often require workers to obtain advanced skills. AET opportunities are more often pursued by high-income and high-skilled workers than low-skilled or low-income workers. With the increased prominence of job automation and technological advances in the workforce, low-skilled workers are at risk for fewer opportunities within the labor market. These factors emphasize the importance of providing learning opportunities throughout the life course. In this mixed-methods study, we analyzed 2012/2014 data from the Program for the International Assessment of Adult Competencies (PIAAC) for the U.S., Canada, the Netherlands, Norway, and Sweden to compare participation rates in non-formal education (NFE) by high and low-skilled adults. Countries were selected based on qualitative findings that inform best practices. Additionally, to gain insights of policies and programs that promote NFE, international key informant interviews (n = 33) were conducted. AET policies and programs, along with barriers such as cost, motivation, and time, were explored with key informants. Findings include (1) aging and skills are negatively correlated in all nations of interest; (2) low-skilled adults are less likely to participate in NFE than their high-skilled counterparts; (3) low-skilled workers in Norway and the Netherlands are more likely to participate in NFE than their U.S. counterparts; and (4) NFE is often more acceptable to low-skilled adults due to previous negative experiences with formal education. Using these findings, we discuss successful AET programs in Nordic countries for overcoming barriers.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".