Co-Constructing Knowledge in Uganda: Host Community Conceptions of Relationships in International Service-Learning
Bibliographic record
Abstract
Background: The social justice goals of service-learning programs are often contingent upon strong relationships with host community members. Given this common narrative, it is necessary to extend our understanding of relationships in international service-learning (ISL), particularly as they are conceptualized by host community members. Purpose: This study engaged seven Ugandan coresearchers in a participatory project to examine the community impacts of a long-term ISL program facilitated by the University of British Columbia (UBC) and based in Kitengesa, Uganda. Methodology/Approach: Thematic analysis of photovoice data from photos, interviews, and focus groups reveals key impacts that are premised on friendships, educational relationships, and relationships for social change. Findings/Conclusions: The article illustrates a host community conceptualization of ISL that positions relationships not as a precursor to ISL done well, but as the success in itself. Extending from this study is a critical discussion of the nuanced, social justice–oriented tensions that arise in the participatory research and co-analysis process. Implications: Institutions often assess the impact of ISL and other experiential education programs in terms of student learning, but findings suggest that social justice goals may be better achieved through an emphasis on relationships and knowledge as conceptualized by host community members.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.033 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".