University‐community engagement: a case study using popular theatre
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the use of popular theatre (PT) as a pedagogical tool around which a community service learning (CSL) senior undergraduate course was oriented, specifically assessing the university student learning experience from this work relative to PT processes and CSL objectives. Design/methodology/approach This paper presents a case study based on participant observations made by the course instructor, and reflective journal entries written by the university student participants. Research limitations/implications Educational partnership efforts of this nature require that they be tailored to contingent circumstances: locale, time constraints, spaces of interaction, willingness, effort and abilities of the group partners, and other particularities of community. Given this, we see learning outcomes as replicable, though shaped in various ways by the circumstances of specific situations. Practical implications The paper demonstrates that by recognizing and relying upon the often unnoticed and neglected, strengths of differently‐abled community members – both students from the university, and the clients from the partner social agency – this kind of community service learning team project transforms and enriches traditional academic outcomes. Findings This paper reports on the outcomes of this experience from the student perspective, and highlights themes of boundary‐breaking, pedagogical risk‐taking and changes in understanding of community through the analysis of the student service‐learning diaries and instructor participation. It also highlights some specific difficulties regarding group dynamics and student concerns that can emerge under learning environments like this, where course expectations, direction and outcomes may not be clearly defined at the outset. Originality/value This paper describes a unique fusion of two alternative teaching and learning methods: CSL and PT. This fusion contributed significantly to student creativity and innovation, to their sense of accomplishment and confidence, and especially to their understanding of diversity and connection to community, all of which they take into the world beyond the university. This fusion of pedagogies is seen as a fruitful direction for institutions of higher education seeking innovative paths to learning, while noting that facilitators need to pay close attention to the unique dynamics of such learning environments.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".