From managing fish to managing people: requirements for effective fisheries governance and management in Europe
Notice bibliographique
Résumé
Despite the increasingly successful implementation of stock management under the EU Common Fisheries Policy, managing fisheries in a sustainable, integrated, and coordinated way remains a challenge. In helping to explain the persistent challenge of achieving sustainable outcomes in EU fisheries, we contend that a central reason is an issue of ineffective governance. Improved governance, appropriately designed for Ecosystem Based Fisheries Management (EBFM), is key to improving the system performance towards the societal objectives. We understand governance as a social process involving the interaction of governments, regional authorities, private industry, and civil society that collectively work towards steering the sector towards sustainability. This encompasses politics, policies, laws, norms, values, regulations, and institutions that guide the management and conservation of fishery resources. Meanwhile, fisheries management involves specific actions and strategies used to manage and conserve fishery resources, including implementing the rules and regulations set forth by fisheries governance and applying scientific principles to ensure sustainability of fish stocks. Historically, fisheries management has focused on the ‘thing’ being managed: fish. Such a focus has resulted in various technical and managerial outputs such as quotas, TACs, catch limits, gear sizes, MPAs, inter alia. Various institutions have also developed (e.g. Advisory Councils) alongside public policy measures such as the Common Fisheries Policy. While progress has been made in recent years, managing fisheries in a sustainable, integrated, and coordinated fashion remains a challenge. The question therefore is: what are the requirements for EU fisheries governance (and hence, management) to be effective? Trying to understand what makes fisheries management more effective (i.e. sustainable), requires us to ask questions about fisheries governance: to what extent are all relevant actors included in decision-making, able to speak to one another, coordinate activities, and work together to resolve key fisheries-related challenges? And what is the capability of actors to observe, define, and understand problems? To answer these questions, we developed and deployed an expert elicitation survey, informed by key governance dimensions and frameworks (the Aquaculture Governance Indicators and the Canadian Fisheries Research Network framework), sent to 245 respondents across selected regional seas (North Sea; Western Waters; Baltic; and Mediterranean) as part of the SEAwise project. In this presentation, we showcase results and analysis from this survey and reflect on how a human-centered governance approach can inform fisheries management, identify ‘weak spots’ that need attention to improve/make fisheries management more effective and address challenges. We argue achieving sustainable outcomes in Europe’s fisheries been a persistent challenge partly because of insufficient focus on the other dimension of fisheries management: people.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,024 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,010 |
| Communication savante | 0,016 | 0,012 |
| Science ouverte | 0,002 | 0,011 |
| Intégrité de la recherche | 0,007 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».