Essential Participants: Centering the Experiences of Southern Hosts in Global Service-Learning Pedagogy and Practice
Bibliographic record
Abstract
In this paper we are concerned with the ways in which hosts are often excluded from scholarship and programming of global service learning. By global service learning (GSL), we mean a multiplicity of programs that occur facilitating service work for people across borders, generally with volunteers moving from the North to the South. We present findings from a research project conducted in 2014 with 37 host families. We circulated a survey to better understand host experiences of, expectations of, and hopes for GSL. Drawing on these survey results we provide some prompting questions for GSL participants (both students and program designers) to shift focus from student experience to relationship and mutuality. Using global service learning literature, critical disability theory and critical pedagogy through an intersectional lens, we center questions of uneven labor, accessibility, and structures of inequity. Three main themes emerged from our data: mutuality, gendered labor, and preparation. We present several infographic images capturing themes from the study to facilitate discussions with students who are preparing for GSL experiences and for those who are leading and designing programming. Our intention is to provide tools for educators to center the voices, desires, and motivations of Southern hosts in all of their GSL preparations.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".