International NGOs and the Role of Network Centrality in Humanitarian Aid Operations: A Case Study of Coordination During the 2000 Mozambique Floods
Bibliographic record
Abstract
In February 2000, Mozambique suffered its worst flooding in almost 50 years: 699 people died and hundreds of thousands were displaced. Over 49 countries and 30 international non-governmental organisations provided humanitarian assistance. Coordination of disaster assistance is critical for effective humanitarian aid operations, but limited attention has been directed toward evaluating the system-wide structure of inter-organisational coordination during humanitarian operations. Network analysis methods were used to examine the structure of inter-organisational relations among 65 non-governmental organisations (NGOs) involved in the flood operations in Mozambique. Centrality scores were used to estimate NGO-specific potential for aid coordination and tested against NGO beneficiary numbers. The average number of relief- and recovery-period beneficiaries was significantly greater for NGOs with high relative to low centrality scores (p < 0.05). This report addresses the significance of these findings in the context of the Mozambican 2000 floods and the type of data required to evaluate system-wide coordination.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".