Security, sustainability and supply chain collaboration in the humanitarian space
Bibliographic record
Abstract
Purpose To extend humanitarian supply chain relationships beyond logistics concerns of delivery, quality and cost. As humanitarian actors continue to face increasing numbers of natural disasters, armed conflicts and attacks on aid workers, security (risk) and sustainability are issues of growing importance. Aiming to inspire discussion, the paper concludes with a research agenda. Design/methodology/approach This is a conceptual paper inspired by relevant statistics, news reports and academic literature. Findings Worldwide natural disasters and armed conflicts are on the rise. So are deliberate attacks on aid workers. Thus, humanitarian supply chain design must include considerations of security and sustainability. Agencies have several options for integrating matters of security and sustainability with the delivery of aid, from being reactive to creating internal solutions to forming proactive relationships with security and sustainability experts. Research limitations/implications There are numerous opportunities for research in the areas of security, sustainability and supply chain relationships. Practical implications Through advocacy and supply chain relationships, humanitarian agencies can enhance security for aid workers and civilians affected by conflict and disasters. Looking to the future, they can also make a positive difference on issues of sustainability. Social implications There is an opportunity to enlarge the “humanitarian space” – and increase security for aid workers and civilians, especially in areas of armed conflict. In the long term, aid agencies can also help eliminate social problems such as gender inequality. Originality/value This appears to be among the first papers to discuss matters of security and sustainability in the context of humanitarian supply chain collaboration.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".