Travelling virtually to Nepal: an innovative pedagogical approach to delivering social work education abroad amid the COVID-19 pandemic
Bibliographic record
Abstract
This article shares findings from a research study that sought to evaluate the strengths and challenges of a virtually delivered, short-term international social work group study program (GSP) to Nepal that took place in Spring 2021 and 2022. This GSP entitled Community and Sustainable Development: Collaborative Field Study in Nepal was delivered virtually, in response to the COVID-19 global pandemic travel restrictions. Despite the obvious drawbacks of not traveling to Nepal, the instructors embraced the challenges as an opportunity to redevelop the course virtually and engage local practitioners, non-governmental organizations, instructors, and students meaningfully by drawing from an anti-oppressive and anti-colonial pedagogy, with an aim to promoting inclusivity, cultural humility, and sustainability. The GSP was led by two instructors from the Faculty of Social Work at the University of Calgary in Canada in partnership with the Department of Social Work at the Southwestern State College in Nepal. This article reviews the relevant literature, describes the methods, details the findings evaluating the model based on the voices of both Canadian and Nepali students and instructors who participated in the GSP. It outlines future implications for social work educators and researchers interested in offering sustainable, inclusive international education.
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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 | 0.000 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".