Engaging Low-Skilled Adults in Education and Training: Exploring Participation Rates, Challenges, and Strategies
Bibliographic record
Abstract
The need for non-formal education (NFE), which does not result in a formal degree or certificate, is substantial as labour markets often require adult workers to take an initiative in advancing their jobrelated skills. Yet, NFE opportunities are more often pursued by highincome and high-skilled adults than their low-income and low-skilled counterparts. For this study, we used data from the 2012 Programme for the International Assessment of Adult Competencies (PIAAC) for Canada, the Netherlands, Norway, Sweden and the US, to compare participation rates in NFE by medium/high and low-skilled adults. Additionally, to gain insights of adult education and training policies that promote NFE, international key informant interviews (n = 33) and document reviews were conducted. Findings include (a) as compared to high-skilled adults, low-skilled adults are less likely to participate in NFE (b) as compared to the US, low-skilled adults in Norway and the Netherlands are more likely to participate in NFE, and (c) NFE is often more acceptable to low-skilled adults, possibly due to previous negative experiences with formal education. These findings are especially relevant to the increased need for retraining and reskilling as a result of the COVID-19 pandemic, which has negatively impacted low-skilled workers more than their higher skilled counterparts (OECD, 2020a).
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".