Understanding the Impact of COVID-19 on the Livelihood Resilience of Small-Scale Fisheries: A Comparative Analysis
Bibliographic record
Abstract
Small-scale fisheries (SSFs) serve as a vital economic cornerstone in many nations, and play a pivotal role in reinforcing food security and eradicating poverty. Despite their significance, SSF systems and the communities they support remain vulnerable, marginalized, and often overlooked. The emergence of COVID-19 and the subsequent lockdowns and restrictions further exacerbated the vulnerability of these small-scale fisheries. These measures effectively halted the routine activities of fishers and traders, resulting in a sharp decline in daily catch, market disruptions, and the inability of households to secure essential food supplies. Additionally, this crisis laid bare the pre-existing vulnerabilities within small-scale fisheries, shedding light on the system's lack of adaptive capacity and resilience among its actors. This study explores the resilience of livelihoods within small-scale fisheries, utilizing the pandemic impacts as a critical stressor pushing the system's actors to their threshold. The aim of this study is to understand the impact of COVID-19 on the livelihood resilience of small-scale fisheries, and to identify the key adaptive responses and factors leading to their successful implementation. \nTo achieve this aim, I assess the impact of COVID-19 on the livelihood resilience of small-scale fisheries communities employing a comparative analysis of six case studies. These case studies feature six countries that experienced substantial impact on their SSFs, namely Malaysia, India, Bangladesh, South Africa, Senegal, and Canada, all of which are integral components of the Vulnerability to Viability (V2V) Global Partnership Research Project funded by SSHRC. The case study analysis was grounded in the Social-Ecological Regime Shifts Analytical Framework. This framework consists of six elements that are essential to address when analyzing a social-ecological system experiencing a regime shift due to an external stressor. The outcomes of the comparative analysis offer an in-depth understanding of how COVID-19 has impacted the various actors within SSF value chains and their responses to this unprecedented disruption. Additionally, the analysis helps determine the scales within the system that reached critical thresholds, providing valuable insights for suggested interventions to mitigate these impacts. Furthermore, the analysis identifies the actual scales of intervention tackled by governments and communities. \nBy comparing the suggested and the actual scales of intervention, the study identifies the five key adaptive responses that have been most effective, namely, consumer-base shift in fish marketing, Alternative Seafood Networks (ASNs), Government aid, sensitive regulations, and community-based approaches. Moreover, the study identified factors leading to the success or failure of these strategies. These factors facilitate long-term interventions such as adaptability, alternatives, knowledge, and tools. These findings contribute to the best practices in governance, coping, and adaptation strategies that can bolster the adaptive capacity of Small-Scale Fisheries. Furthermore, the outcomes inform policymakers, stakeholders, and governments of the essential factors to transform to adaptive governance. This research enhances our understanding of the vulnerabilities exposed by the pandemic and what contributes to the resilience and sustainability of these vital systems and the communities that depend on them.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".