“A Success Story that Can Be Sold”?: A Case Study of Humanitarian Use of Drones
Bibliographic record
Abstract
Increasingly, humanitarian organizations across the globe have been implementing innovative technologies in their practice as they respond to the needs of communities affected by conflicts, disasters, and public health emergencies. However, technological innovation may intersect with moral values, norms, and commitments, and may challenge humanitarian imperatives. Through the examination of an empirical case study on drone mapping, this paper aims to explore three questions: (1) What are the dynamics between aid delivery and technological innovation in the humanitarian enterprise? (2) How are structural problems addressed in an environment in which technology is being portrayed as a force for change? (3) What moral responsibilities towards vulnerable populations should humanitarian stakeholders bear when introducing innovative technologies in humanitarian action. Discussion revolves around the ideology of “technological utopia”, and the normative role of technology in the aid sector - to make substantive impacts, or to produce “success stories”. In conclusion, a call for rigorous ethical analysis to help foster value sensitive humanitarian innovation (VSHI) is made.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.016 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".