Bibliographic record
Abstract
Recent developments in mathematics, science,engineering and technology over the last several decadeshave made it possible to transform technology frommachine-oriented (designed to increase productivity andquantification) to more human-centric (to simplifyinteraction with technology and improve the well-being ofindividuals). Technology is being positioned to be movedfrom a privilege to a social benefit. Such humanitariantechnology (HT) can help at several levels, including: (i)natural disasters (such as fires, storms, tornadoes,tsunamis, earthquakes), (ii) humanitarian disasters(genocides, wars, non-democratic elections, injustice), (iii)developing countries (water, food, shelter, energy,sanitation, health, (iv) developed countries (the poor,seniors, people with physical or mental disabilities, orunder-represented). Since, in all those levels, informationgathering and distribution is considered as important aswater, food and medicines in disasters, it must beconsidered as an ecosystem.The development of a good HT has many scientific,engineering, technological and social challenges. One ofthe important challenges is education. How do we teachthe new generation of humanitarian design engineers? Thispaper describes one approach used in Manitoba.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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".