Toward a social justice model of inclusion for university–community engagement
Bibliographic record
Abstract
University-community engagement often professes to center the shifting needs and issues that communities face but these are often pushed to the periphery in knowledge creation. The methods of engagement, research agenda, outcomes, as well as the measures of success are typically grounded in the academe and universities can almost always pinpoint immediate benefits for the co-creation of this knowledge. Universities are usually able to secure funding and gain prestige to undertake these engagements and projects are put forth as testaments of universities commitments to social change. Communities often struggle to do the same and many are often not equipped with the resources to translate the new knowledge into practice-based initiatives such as programming, funding applications for staffing, services and service delivery. While there are unquestionable merits in the longstanding histories of university-community engagements, more needs to be done for research to be mutually beneficial for both parties. This article outlines five key principles of social justice and inclusion as a preliminary stage of a conceptualized first step necessary to frame university-community engagement. It reiterates consultations with communities through research as a requirement to develop an actual model of university-community engagement to embody social justice and inclusion for true social 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 | 0.051 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.021 | 0.127 |
| Scholarly communication | 0.028 | 0.030 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".