The Times They Are a-Changin’: Time for a Major Emphasis on the Three Ls of Lifelong Learning at Canadian Universities
Bibliographic record
Abstract
This article contends that university continuing education is in need of a dramatic repositioning in the minds and wallets of most university administrations. In order to respond both to a developed economy’s need for the continuous upgrading of skills and knowledge and to universities’ needs for new funding sources, the provision of lifelong education and training—lifelong learning—needs to be strategically central to a university’s vision, mission, and goals. Right now, in its non-degree form, it is a peripheral activity making only minor contributions to universities’ reputation and revenue: according to the Association of Universities and Colleges of Canada in its 2008 report Trends in Higher Education—Volume 3: Finance, only $300 million was earned by universities in noncredit courses. This had not changed much in a decade. Canadian universities are missing out on opportunities in reputation, revenue, and relevance, both domestically and globally. The article goes on to suggest the steps needed in the development of an effective lifelong learning strategy. Some would require changes in university management processes and philosophy to be effective, but continuation of the present half-hearted approach will not succeed in serving either Canada’s lifelong learning needs or its universities’ needs for relevance and revenue.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.043 | 0.015 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 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".