The Research Network on The Changing Nature of Work and Lifelong Learning [WALL]
Bibliographic record
Abstract
(WALL) found that more than half of all Canadian adults and over 60 % of those employed would be more interested in enrolling in further education if their prior informal learning and work experiences were recognized. The survey also found that more than 4 million Canadian adults wanted to participate in further formal education last year but faced barriers to enrolling. In addition, approximately 3 million Canadians not enrolled in adult education would participate if their previous knowledge and experience were recognized. This staggering number of potential participants in adult education represents more than a quarter of the Canadian adult population. This national survey of over 9,000 respondents found that more than two-thirds of people of colour, new Canadians and young people with incomplete high school express a high interest in enrolment in adult education and training if their prior informal learning were to be recognized. All of these groups are found to be already devoting very substantial amounts of time to informal learning. Based on the obtained results it is likely that PLAR may offer part of the solution to the barriers to formal adult education and training faced by many adult learners.
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.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".