Service-Learning as Entry Into or Enhancement of University Volunteering? Student Characteristics at an Elective Service-Learning Institution
Bibliographic record
Abstract
Service-learning is a pedagogical practice that enhances university coursework through volunteering. Current challenges for the field are understanding the benefits of service-learning in relation to volunteering and with regard to pre-service student characteristics. Although students are the focus of service-learning research and practice, understanding how institutions structure service-learning is needed to appreciate its benefits. A model of institutional structuring of service-learning (offered or not, elective or mandatory) is presented. The model is used to inform a study of the academic, psychological, and prosocial characteristics among 266 undergraduate students enrolled in an elective service-learning course at a single large Canadian public university. The study revealed four groups of students: (a) service-learners with prior volunteer engagements, (b) volunteers, (c) non-volunteers, and (d) service-learners with no prior volunteer engagements. The paper is the first to identify and examine service-learners with no prior volunteer engagements and to situate these students in the context of other service-learners, volunteers, and non-volunteers. Although service-learners with prior volunteer engagement resembled volunteers, service-learners with no other volunteer engagement differed from all other groups. The findings are discussed with regard to the benefits of service-learning and volunteering in a variety of institutions.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".