An International Perspective on the Impacts of COVID-19 on Adult Education and Training
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has caused one of the worst economic crises since the Great Depression and the current recession has been more detrimental to older workers compared to other age groups. Not only has it forced more older workers out of their jobs, but it has also made it much harder for jobless older workers to find a new job. Furthermore, due to increased automation and digitalization in the workplace, older workers will likely need upskilling or reskilling to improve their employment prospects in the changed labor market. This situation brings the importance of offering training and continuous education programs that target older workers to the forefront of adult education policy and practice. This qualitative study examines measures taken in response to COVID-19 in adult education and training (AET) in seven countries including Sweden, Norway, the Netherlands, Australia, Singapore, Canada, and the United States. The findings are based on key informant interviews with international policy experts and scholars in the field of AET in addition to information gathered from written materials (e.g., government and organizational reports). To expedite their economic recovery and improve labor market outcomes for their workers, some countries have increased government funding for vocational and continuing education or offered financial support for post-secondary students while others have provided funds to employers to offer training and retraining for their employees. Some of these measures have the potential to expand adult educational opportunities in the post-pandemic world. Implications for policy and practiced are discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".