From managing fish to managing people: requirements for effective fisheries governance and management in Europe
Bibliographic record
Abstract
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.
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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 | 0.024 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".