The Rising Tide of Outreach and Engagement in State and Land-Grant Universities in the United States: What are the Implications for University Continuing Education Units in Canada?
Bibliographic record
Abstract
In this paper, we describe the outreach and engagement movement in the United States and explore the implications of this movement for university continuing education units in Canada. Across the United States, major universities have adopted the vocabulary of “outreach and engagement” to foster a shift in the relationships of those universities with communities and organizations beyond the traditional boundaries of the institution. This vocabulary has its roots in the work of Ernest Boyer (1990, 1996) and the Kellogg Commission on the Future of State and Land-Grant Universities (1999, 2000). In the past decade, many American universities have adopted new leadership and organizational structures to make an operational commitment to outreach and engagement. In Canada, university continuing education units have traditionally been involved in activities that fit within the concept of outreach and engagement, and leaders of such units should consider the implications of the outreach and engagement movement.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.030 | 0.014 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".