A University Without Walls: Connecting the Traditional Classroom to the Community Through Sustainable Service-Learning
Bibliographic record
Abstract
Many worldwide initiatives demonstrate how education has reoriented to address sustainability, encourage university professors to leave their ivory towers, and connect the traditional classroom to civic development by providing society with services. Significant strides have been made to support and optimize service-learning for several academic disciplines in the humanities. However, the education for sustainable development in literary studies in the English department at Sultan Qaboos University in Oman has remained more a dead letter than a radical shift in education. Since a growing body of research evidence has demonstrated that the pedagogical practices relying on traditional teaching are not always effective in engaging students in active participation in learning, the study promotes a novel educational framework as a learning process whereby the wall between the university and the community is broken down, English literary studies are paired with sustainable service-learning, and knowledge is created through the transformation of students’ experiences. Within this framework, the pairing of service-learning and literary studies is an innovative way to teach course concepts, expand students’ vision of the society, and empower citizens with the knowledge, skills, values, and attitudes to achieve sustainable development in Oman and set the example for other Gulf countries.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".