Non-Formal Adult Learning Programs at Canadian Post-Secondary Institutions: Trends, Issues, and Practices
Bibliographic record
Abstract
A number of recent policy reports have suggested that Canadian universities and community colleges should play a more significant role in response to the adult education and training needs of Canada’s workforce. This article discusses the results of a study that examined investment trends and the characteristics of non-formal adult learner programming at Canadian postsecondary institutions. Public universities and community colleges were surveyed, and a purposive sample of key informants, representing the broad spectrum of postsecondary education in Canada, was interviewed. The results indicated that institutional investments in non-formal programs for adult learners have trended upward over the past decade. Colleges reported larger average annual institutional expenditures on and larger enrolments in non-formal adult learner programs. However, adult learners comprise only a small minority of the overall student population at post-secondary institutions. Financial barriers at both the institutional and individual levels were identified as key barriers to increasing access and participation. Limited operational funding at the institutional level has influenced the nature and scope of offerings and, for many institutions, has resulted in program offerings that do not necessarily target the needs of nontraditional and disadvantaged adult learner groups. The study findings have important public-policy implications for improving access and participation in non-formal adult learning, including the need for greater incentives for individuals (e.g., tax incentives) and increased support for disadvantaged learners to enhance basic-skills training.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".