From Sudbury to Sogog: Stories from a Canadian Student's Health Promotion Without Borders Excursion to Mongolia
Bibliographic record
Abstract
As the popularity of International Service-Learning (ISL) excursions continues to grow, there is an increasing need for research that explores these types of experiences. This manuscript focuses on the experiences of the lead author (S.L.D.) while participating in an ISL excursion offered by the Health Promotion Without Borders (HPWB) Program as part of their graduate research. The HPWB Program has facilitated ISL excursions for students in the School of Kinesiology and Health Sciences (SKHS) at Laurentian University (LU) in Canada for over two decades. However, there is limited formal research about the experiences of HPWB participants while completing their ISL excursions. This research addresses this need by using an autoethnographic approach to explore the lead author's HPWB experience. During the lead author's excursion, they confronted many moments of cultural dissonance, which challenged their usual way of thinking. Through critical reflection after their excursion, the lead author realized the defining role those moments of cultural dissonance had on the nature of their ISL experience. The lead author wrote six stories to share their understanding of those cultural dissonance encounters and provide a snapshot of their excursion for the reader to make sense of in their own way. Overall, this research may benefit future ISL participants and coordinators and adds to the sparse literature available on the nature of ISL experiences from the participant perspective using an autoethnographic method.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.048 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".