Individual Learning Accounts: A Comparison of Implemented and Proposed Initiatives
Bibliographic record
Abstract
Access to lifelong learning opportunities has long been discussed in terms of the economic benefits conferred by access to and engagement in further education by members of the labor force, particularly within the global knowledge economy. However, equitable access to lifelong education opportunities, particularly for low-skilled adults in the labor force, has been lacking. The Organisation for Economic Cooperation and Development (OECD) identified three models for funding adult learning: (1) individual learning accounts, (2) individual savings accounts, and (3) training vouchers. The current study discusses examples of these models, either proposed or implemented, across four countries or economic blocks—France, Canada, the United Kingdom, and the United States. In addition, to understand the importance of providing funding for education and training to adults with low levels literacy skills, we use data from the Program for the International Assessment for Adult Competencies (PIAAC) to compare participation in adult education and training (AET) by literacy skill levels. In all countries examined, adults with low literacy skills participated in AET at lower rates than those with middle and high levels of literacy skills. To be successful in reaching adults most in need of skill upgrading, financing models need to provide adequate funds for meaningful skill upgrades, have well-structured information sources (e.g., websites) that are easily navigated by the target population, and include policies to screen educational providers for program quality.
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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 | 0.020 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".