Coordination and relationships between organisations during the civil–military international response against Ebola in Sierra Leone: an observational discussion
Bibliographic record
Abstract
The Ebola virus disease (EVD) crisis in West Africa began in March 2014. At the beginning of the outbreak, no one could have predicted just how far-reaching its effects would be. The EVD epidemic proved to be a unique and unusual humanitarian and public health crisis. It caused worldwide fear that impeded the rapid response required to contain it early. The situation in Sierra Leone (SL) forced the formation of a unique series of civil-military interagency relationships to be formed in order to halt the epidemic. Civil-military cooperation in humanitarian situations is not unique to this crisis; however, the slow response, the unusual nature of the battle itself and the uncertainty of the framework required to fight this deadly virus created a situation that forced civilian and military organisations to form distinct, cooperative relationships. The unique nature of the Ebola virus necessitated a steering away from normal civil-military relationships and standard pillar responses. National and international non-governmental organisations (NGOs), Department for International Development (DFID) and the SL and UK militaries were required to disable this deadly virus (as of 7 November 2015, SL was declared EVD free). This paper draws on personal experiences and preliminary distillation of information gathered in formal interviews. It discusses some of the interesting features of the interagency relationships, particularly between the military, the UK's DFID, international organisations, NGOs and departments of the SL government. The focus is on how these relationships were key to achieving a coordinated solution to EVD in SL both on the ground and within the larger organisational structure. It also discusses how these relationships needed to rapidly evolve and change along with the epidemiological curve.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".