Impact Assessment. Community-engaged Research (CER) at the University of Victoria, 2009-2015
Bibliographic record
Abstract
This Impact Assessment report is based on several consultations and research (empirical and document analysis) that took place between July – December 2016 with former Directors, Associate Directors and Research Affiliates from the Office of Community-based Research (OCBR) and the Institute for the Studies and Innovation in Community University Engagement (ISICUE) at the University of Victoria. This assessment is prepared for the Office of the Vice President Research (OVPR) by the Office of Community University Engagement (OCUE), in partnership with Research Partnership Knowledge Mobilization (RPKM) unit at the University of Victoria (UVic). The main objective is to assess the various levels (e.g. micro, messo, macro) and broad range of impact resulting from Community-Engaged Research between 2009-2015. This includes direct outputs and outcomes of the OCBR (2008-2012) and ISICUE (2012-2015), as well as a full academic unit scan across the campus drawing from the Enhanced Planning Tool document (2014-15). Impact is documented by 5 indicators including: 1) external research funding, 2) academic unit scan, 3) reputation, 4) 12 indepth impact case studies, and 5) community-engaged learning metrics. The occurrences of impact are applied to OCUE’s 5 pillars of engagement: Community-engaged Research, Community-engaged Learning, Knowledge Mobilization, Good Neighbour and Institutional Policies and Support, the United Nations Sustainable Development framework (17 goals), as well as UVic’s International Plan (4 areas). The results point to a wide range and diversity of impact to society in each of the 5 OCUE pillars across the academic units in almost all the UN Sustainable Development Goals. Impact narratives from 12 in-depth case studies across the campus (e.g. Business, Engineering, Geography, History) demonstrate signifcant institutional and community beneft as an outcome of CER. The results highlight key institutional supports (e.g., RPKM, ORS) and provide an enhanced understanding of key contextual features of successful Community-engaged Research (CER) initiatives. The results inform criteria to support the assessment of community engaged scholarship in reviewing grant applications, partnership proposals, and faculty tenure, promotion, and merit applications. An impact rubric and guidelines for promotion and tenure are a valuable outcome of this project. This assessment is not exhaustive of all CER activities on campus. Appendix II provides some insight into the numerous research partnerships excluded from this study due to not having enough information that ft the criteria (See methodology).
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".