The Rise of Humanitarian Engineering Education in Australasia
Bibliographic record
Abstract
Context: Since the early 1980's, numerous organisations seeking to utilise engineering to address humanitarian and development challenges have been established including Engineering for Change, Engineers Against Poverty, Engineers for Overseas Development and national Engineers Without Borders and RedRs. This has contributed to the growth of humanitarian engineering education programs and initiatives in countries including the USA, UK and Canada from the early 2000's. Similarly, humanitarian engineering education courses and initiatives have been established in Australian and New Zealand. Purpose: This paper details the growth of humanitarian engineering education programs and initiatives in Australasia since 2006 leading to the current state of the field. From this opportunities for further growth and development will be identified. Approach: Student and university participation data drawn from national programs as well as details of current and planned university offerings is used to identify the growth in humanitarian engineering education in Australia and New Zealand. Outcomes from a collaborative cross-institutional workshop are used to identify priorities and opportunities for growth and development. Results: Although isolated initiatives have been delivered under a variety of terms, the current growth of humanitarian engineering education dates back to the launch of the EWB Challenge in 2007. Since 2015 there has been a dramatic increase in the scale of offerings and engagement with the establishment of the EWB Humanitarian Design Summits and introduction of Australian Federal Government support for mobility programs. This has led to the development of elective courses in the area and formal award programs emerging from 2016, with at least five Australasian universities offering or planning award programs. Broader impact is demonstrated by student demographic data which clearly indicates a significantly higher percentage of female engagement in the area than typical for engineering. Conclusions: Opportunities exist to continue to expand the field and its impact including educational research and development, engagement with professional bodies, and advocacy. This will contribute to leadership and the potential for humanitarian engineering to achieve positive impacts for communities and individuals in Australasia and internationally.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".