Understanding the Impact of COVID-19 on the Livelihood Resilience of Small-Scale Fisheries: A Comparative Analysis
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».