Bibliographic record
Abstract
Refugee communities exhibit remarkable innovation as a means of survival, yet engineering’s engagement with refugee communities often comes from a deficit-perspective, where we impose our technologies onto their communities. This research seeks to shift perspectives from a deficit-based view to an asset-based approach by highlighting and celebrating these communities' innovations and experiences (Gravel et al., 2021). Specifically, we interviewed student refugees to understand what engineers can learn from refugee innovation. Using qualitative methodology and engaging participants throughout in a co-design process, the data was analysed through qualitative content analysis (Elo & Kyngäs, 2008). These interviews offered valuable insights into refugees' daily lives, survival strategies, and engineering innovation. The stories shared illustrate how these existing skills can be cultivated within refugee camps, particularly when development opportunities are provided by organizations such as the UN. These opportunities contribute to the resilience of individuals and help strengthen the communities they are rebuilding. By exploring refugee experiences and innovations, this research promotes a broader perspective on community-driven engineering and asset- based understandings of refugee communities. Future work could be used in engineering classrooms, allowing students to engage with diverse perspectives and create more effective, culturally relevant engineering solutions for the communities they serve.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".