Reinventing Universities: Continuing Education and the Challenge of the 21st Century
Bibliographic record
Abstract
Canadian universities are in the midst of a lengthy period of financial uncertainty and public pressures to change, circumstances that add to the pressures on continuing education units and create opportunities for innovative change. The emergence of MOOCs, demands for research relevance, and concerns about the employability of graduates have forced campuses to consider new approaches, implement alternative financial models, find additional revenue, and search for efficiencies.In this environment, continuing education professionals have significant opportunities to provide to the campus-wide university, even after years of being marginalized on many campuses. Continuing education units work with external audiences and clients, have experimented with new revenue sources, have explored and evaluated distance delivery/ technology-based methods, and have become accustomed to living with constant change.While it will be difficult for continuing education units to attract campus-wide attention, particularly from traditional academic disciplines, there is a strong likelihood that universities as a whole will need the insights, strategies, approaches, pedagogy, and business models that have evolved in the outreach divisions in recent decades.
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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".