Community-University Engagement in an Electronically-Defined Era
Bibliographic record
Abstract
Universities can enhance the return on the public investment that they represent by collaborating with their natural allies in addressing pressing social issues. That work can be further enhanced by harnessing appropriate digital technologies. In this chapter, the authors profile a current example of a community-led, multi-layered partnership that was formed to strengthen the infrastructure of the charitable sector in Canada. In particular, the chapter demonstrates that the “habit of partnerships” combined with the “habit of technology” is a potent strategy for addressing community needs. The authors argue that no single partnership or technology will transform the academic enterprise, but rather that the widespread adoption of technologies among universities’ allies, competitors, students, and faculty that characterizes the electronically-defined era will compel universities to adopt both the habit of partnerships and the habit of technology. That, in turn, will transform the way universities do their business and those with whom they do it.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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".