From the Ground Up: Critical Reflections About Co-Constructing A New Non-Profit Sector Undergraduate Certificate
Bibliographic record
Abstract
Community-university engagement is a growing field and takes many different forms. We explain and reflect critically on a community-university developmental process that we created to design a new non-profit studies undergraduate certificate. A steering group comprising students, non-profit organizations (NPOs), and faculty guided our process. We adopted a community-based, emergent, multi-tactic process that went from testing an idea, to collectively designing and co-constructing the certificate to building momentum to operationalize it, over an 18-month period. Our strategy was based on the convergence of three main bodies of literature—community-engaged scholarship, citizen participation, and naturalistic inquiry—and included seven tactics: community-university dialogues, e-communication, interactive booths in public places, presentations and learning circles, student research projects, student and NPO surveys, and pilot-testing undergraduate courses. The outcomes of our process revealed strong community support for a new certificate, which was then co-constructed and later approved by the University Senate. Today, five years later, we reflect on the ebb and flow of our process, in particular: emergent design challenges, the space-in-between, community/university black boxes, ownership, and facilitation work. This exploration contributes to the knowledge base on co-construction processes.
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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 | 0.047 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.046 | 0.063 |
| Scholarly communication | 0.028 | 0.018 |
| Open science | 0.005 | 0.034 |
| Research integrity | 0.011 | 0.029 |
| Insufficient payload (model declined to judge) | 0.003 | 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".