The Making Research Accessible Initiative: A Case Study in Community Engagement and Collaboration
Bibliographic record
Abstract
Recently community engagement has emerged as a priority among universities, offering new opportunities for their libraries. A literature scan of community-centred work in libraries reveals diverse examples but a lack of conceptual definitions or frameworks to help practitioners advance their work for social impact. We present a case study using the Carnegie Foundation definition of community engagement and apply two conceptual frameworks: living lab constructs and boundary spanning theory. The living lab constructs provide a framework to describe an innovation process that addresses a social challenge, experiments with specific actions for change, and defines specific returns or social impact. Boundary spanning theory provides a framework to help university leaders conceptualize linkages to community in ways that account for institutional complexity and foster reciprocal, mutually beneficial relationships with community partners. We use these two frameworks to describe the Making Research Accessible initiative which has three goals: i) increase the accessibility and impact of research done in the community; ii) increase the availability to researchers of community-generated research; iii) create opportunities for community and university members to share information and learn from each other. From the case study, we summarize what we have learned about community engagement to be of general relevance to library practitioners.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.034 | 0.017 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.010 | 0.007 |
| 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".