A Framework for Justice-Centering Relationships: Implications for Impact in Place-Based Community Engagement
Bibliographic record
Abstract
Community engagement in higher education has been promoted as critical to fulfilling higher education’s responsibility to the public good through teaching, learning, and knowledge generation. Reciprocity and mutual benefit are key principles of community engagement that connote a two-way exchange of knowledge and outcomes. However, it is not clear from existing literature whether community engagement impacts communities in positive ways. The problem addressed through this study was how campus-community partnership stakeholders define impact. Using grounded theory, the ways community and campus partners defined, measured, and understood community impact in a diverse set of campus-community partnerships at two U.S. urban, Jesuit universities that employ place-based approach to community engagement were explored. Relationships as facilitators of impact and as impacts in and of themselves emerged as central themes. Themes from the data led to the development of the Justice-Centering Relationships Framework which includes two paradigms for understanding community impact in higher education community engagement – Plug-and-Play and Justice-Centering Relationships – that are bridged by a Reframing process. The Framework contributes to and informs the “how” of taking a place-based community engagement approach that leads to positive benefits for community impact, student learning and formation, and institutional change.
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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 | 0.040 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.016 | 0.094 |
| Scholarly communication | 0.019 | 0.027 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".