The Café Concert Project: music programming for student engagement at Memorial University
Bibliographic record
Abstract
With a deep respect for the student voice, this study explores the Café Concert Project, a music-based initiative designed to enhance student engagement and foster a sense of belonging within the university community. Situated within Canadian student affairs, the research examines how music programming can create meaningful connections and support students' emotional and social integration into university life. This study highlights the critical role of belonging and community in enhancing engagement outcomes by centring the student voice through collaborative autoethnography. Grounded in student development theories that emphasise student involvement and the significance of social integration in student retention and informed by the practices of leading music-in-community researchers, this project investigates how music-making on campus bridges student affairs and community-building. Using individual narratives and a collective social lens, the study employs collaborative autoethnography as data to capture both personal and shared experiences. Through examining student experiences in the Café Concert, this research provides valuable insights into how arts-based engagement initiatives can actively foster a sense of belonging and build inclusive communities on campus. The findings suggest that music-making strengthens students' social connectedness by creating shared experiences and serves as a meaningful support system that enhances their university journey. By creating a space where students feel connected and valued, the Café Concert Project demonstrates the potential of music programming to positively impact students' academic and personal lives, reinforcing their integration within the university's social fabric.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".