Individual Learning Accounts: A Comparison of Implemented and Proposed Initiatives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it