Undergraduate students’ perceptions of community engagement: A snapshot of a public research university in Canada
Bibliographic record
Abstract
Students who participate in regular community engagement (CE) often experience benefits in different areas of their lives. Many academic institutions have implemented action plans to increase CE within the student population. At the University of Calgary, Canada, this is done primarily through its broader Eyes High strategy. As there remains a gap in the literature about students' perceptions of CE and their awareness of university CE strategies, this study aims to identify undergraduate students’ awareness of the Eyes High strategy, attitudes towards and levels of engagement, and challenges and potential methods to increase CE participation. Data was collected through a voluntary online survey (n=528). Participants were recruited through posters, social media, online faculty platforms and by faculty members. Survey results indicated students lacked knowledge regarding the Eyes High strategy. It was noted that students’ knowledge, attitudes and practices of CE increased as they spent more time at the university. The top perceived challenges to CE were lack of time, accessible information, support and incentives. To increase accessibility and student participation, we suggest introducing the Eyes High Strategy and CE activities early to the undergraduate population through workshops, credit-based courses and/or professional development requirements. Our data suggests that students are not well informed about the Eyes High strategy. There is thus a need for the university to build a campus-wide, student-informed initiative to proactively engage students. This research will serve as a gateway to further explore communicative methods that might better convey university priorities to students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".