‘Engineering for Global Development’ in Academic Institutions: An Initial Review of Learning Opportunities Across Four Global Regions
Bibliographic record
Abstract
Engineering for Global Development (EGD) is a growing field in which technology development, design research, and implementation sciences are utilized to support communities worldwide and improve their quality of life. The study area is known by various terminologies worldwide, such as ‘global engineering,’ ‘frugal engineering,’ ‘sustainable development engineering,’ ‘humanitarian engineering,’ and so on. This report aims to provide an initial overview of the breadth of EGD-related opportunities available at universities through a series of regional reviews in four major global regions: (1) Canada and the United States, (2) Australia and New Zealand, (3) Latin America, and (4) Asia. In addition to documenting the types of EGD programs offered at academic institutions, we aim to analyze the ways in which EGD-related educational opportunities are offered across the specified global regions. Importantly, we review and compare common terminologies used in each region. Across the regions explored, we identified 85 intuitions with various EGD-related learning opportunities, primarily in the form of experiential learning and research opportunities. Some universities offer degrees and certificates, but the quantity of degree-granting programs remains relatively small. Future work should explore this increase in demand and quality of available training, especially relevant to SDG 4: Quality Education. Our findings indicate that a robust EGD academic community exists globally. Notably, institutions use a variety of terminologies, and similarities and differences in their usage exist regionally. Ultimately, this report aims to comprehensively review the many terminologies, programs (e.g., undergraduate courses, masters, postgraduate, and innovation centers), and research groups dedicated to EGD-related academic work across the four regions specified. The findings from this study contribute to the growing literature and efforts to expand EGD endeavors and collaborations in academic institutions globally.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".