From Community Engagement to Community Emergence: The Holistic Program Design Approach
Bibliographic record
Abstract
University-community engagement has the potential to positively transform higher education, but community-engaged institutions must overcome challenges related to defining, planning, and assessing engagement activities. The 2015 Carnegie Community Engagement Classification application process and the results of the 2015 Campus Compact member survey revealed that there is room for improvement in engagement efforts within public and private institutions alike. The authors propose the holistic program design approach to curricular-based engagement as a new framework for building individual and institutional capacity. Utilizing interactional field theory, the framework shows how university-community engagement can promote the emergence or formation of community between a university and local participants. Curricular-based engagement experiences serve as venues for interaction in which students, faculty, and local residents communicate and work to address common, place-based needs. The authors provide operational definitions of university-community engagement and curricular-based engagement, describe a theoretical and philosophical rationale for engagement, and present a conceptual model of student and community development outcomes. They also highlight potential assessment metrics, address five recommendations of the Carnegie Foundation, and suggest directions for future research and development.
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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 | 0.019 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".