Exploring Global Responsibility During an Immersive International Design Experience
Bibliographic record
Abstract
Global responsibility in engineering involves critical reflection on the role of technology in society, acknowledging the social, political, and economic impact of engineering decisions. Effective humanitarian engineering endeavors require engineers who understand these considerations. While more universities offer curricular and extra-curricular experiences aimed at improving students' sense of global responsibility, there remains a gap in research assessing the effectiveness of these experiences. This study examines one such initiative: a Design Summit hosted by Engineers Without Borders (EWB)-Australia, a four-week immersive design experience for undergraduate students from Australia and New Zealand. Using phenomenological and ethnographic methods, we will explore how students' perspectives on global responsibility evolve throughout the summit. Data collection includes pre- and post-program semi-structured interviews and ethnographic observations during the summit. Analysis will be guided by identity development theory to uncover factors that influence change in perspectives and understanding of responsibility in humanitarian engineering contexts. Data will be gathered between June and August of 2025. This presentation will share preliminary findings and emergent themes, offering insights for educators and program designers seeking to integrate global responsibility more effectively into engineering 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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".