The <i>community engagement for impact (CEFI) framework</i> : an evidence-based strategy to facilitate social change
Bibliographic record
Abstract
Higher education’s focus is shifting to include societal impact alongside academic excellence. While community-engaged scholarship has a long history, many initiatives focus on individual researchers or institutional practices, without accounting for disciplinary and geopolitical contexts. The Community Engagement for Impact (CEFI) Framework and the Contextual Model of Community Engagement (CMCE) are based on findings of an in-depth, qualitative study of researchers’ strategies for community engagement. Results point to complex relationships between researchers, universities, and disciplines, shaped by government policy, research trends, community imperatives, and other factors. While participants fostered community relationships supporting social change, they did not receive appropriate training, support, or recognition. CEFI guides individuals and institutions to identify barriers and facilitators for engagement, across disciplines, for work involving industry organisations, community groups, governments, and other partners. When used alongside CMCE’s approach to local, national, and global factors, researchers, universities, and disciplines can better support pathways to societal impact.
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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 | 0.264 | 0.209 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.012 | 0.047 |
| Scholarly communication | 0.035 | 0.026 |
| Open science | 0.012 | 0.051 |
| Research integrity | 0.016 | 0.025 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".