The Ebola epidemic in West Africa: Humanitarian aid and public opinion in Iceland
Bibliographic record
Abstract
Public support of development cooperation in high-income countries, including humanitarian assistance, is important if low-income countries are to be given an opportunity to improve the situation of their population. This research examined public attitudes in Iceland toward humanitarian aid, with the aid provided by the Icelandic government in September 2014 to fight the Ebola epidemic in West Africa as a case in point. Specifically, it examines which demographic characteristics were related to negative attitudes towards the assistance, and which reasons the public believed influenced the decision to provide aid. A questionnaire about attitudes towards the Ebola epidemic was administered to a sample of 1500 adults from an internet panel established by the Social Science Research Institute of the University of Iceland, and 920 people answered (61% response rate). Quarter of the participants expressed negative attitudes towards the humanitarian aid the government provided in response to the Ebola epidemic. Those who held negative opinion were more likely to be less educated, lean to the right in political orientation and have lower household income. People with negative attitudes towards foreign aid were less likely to believe ethical reasons influenced the decision to provide humanitarian assistance. Both those who hold positive and negative views towards foreign aid recognized to some extent the influence of self-interests in providing humanitarian assistance. To enforce positive attitudes towards foreign aid it is important that governments ensure that development cooperation and humanitarian aid are based on ethical considerations, in addition to educating the public about poverty and development processes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".