The role of satellite technologies in relief logistics
Bibliographic record
Abstract
Purpose The devastating impact of catastrophic disasters on terrestrial infrastructure requires the adoption of alternative technology solutions among humanitarian organizations. The purpose of this paper is to analyze the role of the most commonly used satellite technologies in relief logistics: imagery and mapping, portable global positioning system (GPS) positioning devices, telecommunications, and GPS vehicle tracking. Design/methodology/approach The paper examines both the benefits and limitations of satellite technologies in light of the existing literature and through a complementary questionnaire survey with field workers involved in humanitarian operations in the aftermath of the 2010 Haiti earthquake. Findings The results show that the use of satellite technologies can facilitate most of the key logistics challenges encountered by relief actors. However, they also highlight important barriers within humanitarian organizations such as the lack of skilled workers and high costs, underlining the need for long-term training, resource investments, and cooperation between users and technology providers. Research limitations/implications The research findings remain valid only in the context of catastrophic disaster responses, which lead to similar destructions, logistical problems, and needs for satellite technologies. Practical implications This paper shows how satellite technologies can support humanitarian professionals in the field. It also provides policy recommendations that can facilitate the use of these technologies. Originality/value The applications of satellite technologies within humanitarian supply chains are not well-defined in the literature. This paper is the first to be dedicated to analyze the role of the main satellite technologies used in a relief logistics setting.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".