Disasters and long-term economic sustainability: a perspective on Sierra Leone
Bibliographic record
Abstract
Purpose This paper aims to analyse the case of Sierra Leone from the lens of economic impact and underlying causes for concern towards economic sustainability in a post-Ebola recovery phase. Design/methodology/approach Content analysis of literature from various sources, including public and private sectors, non-governmental organisations, multilateral agencies, peer reviewed scholarly articles and media reports was carried out. A total of 77 articles were reviewed. Each document from each source types was then examined for recurring themes that would enhance understanding on the topic addressed here. The NVivo qualitative analysis software was used for coding and extracting of themes from these articles using certain keywords and phrases that relate to the study objectives. Findings The Ebola outbreak in Sierra Leone caused impairment of exports and the capacity to raise revenue via taxes due to significant slump in economic activities. The post-conflict strategy to increase foreign investment had kick-started a gradual recovery, but the Ebola crisis threatened further gains. The crisis also highlighted that the country’s economy depended on foreign investment in a single key sector of iron ore for which global prices fell during Ebola significantly. Although socio-economic impacts of Ebola will linger for some time and health system would have to be vitalised, a sense of optimism was found in many documents. Originality/value The research approach is new and comprehensive in that it looks at post-conflict Sierra Leone in combination with ongoing biophysical and hydrometeorological hazards, and how the Ebola outbreak became completely devastating for the country’s economic sustainability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".