Domestic and International Perspectives on Financing Adult Education and Training
Bibliographic record
Abstract
Abstract Adult education and training (AET) over the life-course is necessary to participate in economic, social, and political activities in the time of globalization and technological advancement. However, little research has been done to identify mechanisms to fund AET opportunities among middle-aged and older adults from a comparative international perspective. Our study aimed to identify strategies to finance AET opportunities for middle-aged and older adults through an international lens, to help identify barriers and facilitators in effort to best support adult learners regardless of education background or socioeconomic characteristics. We carried out a descriptive qualitative study to facilitate an in-depth understanding of funding mechanisms available to adult learners in the selected countries, from the perspective of adult education and policy experts. Data were collected using semi-structured interviews with 61 international adult education experts from government agencies, non-governmental organizations, and education institutions. Our informants represented 10 countries including Australia, Canada, Germany, Italy, the Netherlands, Norway, Singapore, Sweden, the United Kingdom, and the United States. Data included at least one in-depth phone or web-based qualitative interview per informant in addition to information gathered from written materials (e.g., peer-reviewed publications and organizational reports). We identified three financing options that arose as themes: government-sponsored funding; employer-sponsored funding; and self-funding. We found that government-sponsored funding is especially important for low-skilled, low-income older adults for whom employer-sponsored or self-funding is not available. Our results have implications for lifelong AET policy changes, such as adaptations of successful AET funding programs across global communities.
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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 | 0.008 | 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 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".