Bibliographic record
Abstract
Purpose The purpose of this paper is to describe and analyze experiences embedding community service learning into an assignment for a bachelor of social work course. The author used these experiences and their connections with early conceptions of progressive education and community work principles to present a pragmatic and supple community service learning (SCSL) model. Design/methodology/approach In total, 15 students and four community organizations participated in SCSL. Data consisted of focus groups, participatory observation, evaluations, e-mails, and documents. Naturalistic case study methodology was employed to retrospectively describe a noteworthy teaching and learning experience. Findings The SCSL model was judged useful for weaving current local realities into course lectures, promoting professional development, and providing community organizations with timely research syntheses. It seemed no more demanding than other teaching experiences. Six features of the model were deemed beneficial: multi-course scaffolding, bottom-up management, asymmetrical student roles, integration of academic and experiential learning, and student involvement in course delivery. Relevant contextual factors included: small class size, maturity of students, and cohesion within cohort. Research limitations/implications A single teaching experience and a small sample of participants informed this case study. Further research is needed to draw firm conclusions about SCSL’s usefulness and generalizability. Practical implications Acknowledging that it is based on limited evidence, SCSL appears to be a promising model for encouraging knowledge mobilization between universities and community organizations, and providing future professionals with experience in such activities. Originality/value This paper describes and analyzes the pedagogic value of SCSL, a manageable and adaptable teaching model for busy faculty.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".