Aligning Continuing Education Units and Universities: Survival Strategies for the New Millennium
Bibliographic record
Abstract
The goal of the study presented in this paper was to understand and to start to document the contributions that a continuing education unit (CEU) makes to the university. Although continuing education contributes in both financial and non-financial ways, the financial benefits are often the only recognized contribution. The non-monetary contributions are significant, however, and may be the most critical.A national survey of Canadian continuing education deans, conducted by the author, is discussed in this paper. Deans were asked to respond to a list of contributions that were identified by focus groups of continuing education programmers. Deans were also asked to rank each indicator as to its level of importance in gaining support for a CEU within the university. Outcomes were categorized on the basis of their financial contributions and on contributions to the teaching mission, the research mission, and the strategic directions and initiatives of the university. The findings provide evidence of significant contributions in all four categories, although the research contributions are ranked the lowest. CEUs may find the list of institutional outcomes identified in this paper useful in assessing their own contributions and in building support for their units.
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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".