Perspective: Developing Social Security Schemes for Small Island Economies: Lessons from Fiji’s Covid-19 Experience
Bibliographic record
Abstract
COVID-19 has triggered deep economic damage and devastated livelihoods to an extent never before experienced. It has revealed socio-economic vulnerabilities and so can be used as a learning platform in preparing for future shocks. In particular, it has exposed the vulnerability of households to sudden, severe, and prolonged income shock, the significance of social security as a shock response tool, and the importance of household resilience for macroeconomic stability. This study uses the pandemic as an opportunity to understand the resilience of Fijian households to profound and prolonged income shocks, given these households' social, cultural, and economic setting. It evaluates national response strategies, household coping mechanisms, and gaps in the current social security measures in Fiji. This evaluation reveals several key lessons for a systematic response to any future shocks. The lessons may prove beneficial not only for Fiji, but also for other similar economies in the region. Policy makers can build on the operational learning and capacity developed during the pandemic, reinforce existing social security systems, and be better prepared for future income shocks. Fiji and other Pacific Island economies are highly vulnerable to climate-related risks and have endured the adverse economic effects of some extremely intense natural disasters. It is important for these economies to strengthen household resilience and develop sustainable and broad-based programs for social protection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".