Higher Education Institution–Community Partnerships: Measuring the Performance of Sustainability Science Initiatives
Bibliographic record
Abstract
Abstract The enterprise of sustainability science extends beyond the academy to address pressing environmental issues through collaboration. It coincides with trends in higher education institutions (HEIs) towards an expanded mission for addressing societal challenges as well as greater accountability. In this paper, we aim to establish an instrument for assessing the performance of sustainability science initiatives in HEIs. The performance of three HEI–community partnerships for sustainability science in Ontario, Canada (the Brock-Lincoln Living Lab, the Excellence in Environmental Stewardship Initiative, and Niagara Adapts) were examined using the HEI–Community Partnership Performance Index (HCPPI). Our preliminary results suggest that the HCPPI is a reliable, valid, and easy-to-administer tool for accurately assessing the performance of HEI–community partnerships for sustainability science. Incorporating systemic performance assessments into HEI–community partnerships promotes accountability, transparency, and continuous improvement. It also serves as a vital feedback mechanism by fostering reflection, adaptation, and learning—critical components to sustainability science.
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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 | 0.023 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".