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Record W6959957789 · doi:10.11583/dtu.24199137.v1

SEAwise report on requirements for fisheries governance to be effective

2023· other· en· W6959957789 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2023
Typeother
Languageen
FieldMedicine
TopicMedicinal Plant Studies
Canadian institutionsnot available
FundersEuropean Commission
KeywordsFisheries managementCorporate governanceWork (physics)Fisheries lawCommissionEuropean commissionEcosystem approachEcosystem-based managementPlan (archaeology)

Abstract

fetched live from OpenAlex

The SEAwise project works to deliver a fully operational tool that will allow fishers, managers, and policy makers to easily apply Ecosystem Based Fisheries Management (EBFM) in their fisheries. This SEAwise report discusses the concept of governance, how to understand ‘effective’ governance, and a research plan for further studies of the effectiveness of and potential for improving governance at the regional and sub-regional level in the SEAwise regions (Baltic Sea, North Sea, Western Waters, and the Mediterranean Sea). The theoretical insights from the first two main parts inform and are merged into the research plan, forming the last part of the report. The work is based on the recognition that fisheries management in Europe is still struggling to deliver on its objectives relating to ecology, economy, and social considerations although improvements have been made over the last decades. On top of this, marine biodiversity and ecosystem integrity can be identified as pressing challenges, while climate-change presents renewed uncertainties and risks.Improved governance, appropriately designed for Ecosystem Based Fisheries Management (EBFM), is key to improving the system performance towards the societal objectives. Lack of appropriate measures towards cooperation between the EU, national, and regional levels has led to uncoordinated decision-making processes and prevented coherent management through the implementation and adoption of EU legislation, leading to lower than desired performance both of fisheries and environmental policies. Referring specifically to the involvement of stakeholders, the European Commission stresses the importance of transparency, cooperation, outreach, information, and inclusiveness in developing and implementing measures to ensure that all stakeholders, not least fishers, have a say in the management process, and that their needs and concerns are considered (European Commission, 2023a). Improvement of what can broadly be defined as ‘governance’ is, thus, among the pathways that the European Commission has identified for improvements in the area.In SEAwise, we understand governance as a social process, mediated by a variety of social actors: governments, regional authorities, private industry, and civil society. As such, governance is “the whole of public as well as private interactions taken to solve societal problems and create societal opportunities. It includes the formulation and application of principles guiding those interactions and care for institutions that enable them” (Kooiman & Bavinck, 2005, p. 17). Following this, effective governance requires that this constellation of actors is able to speak to each other, coordinate activities, be included in decision-making processes, and work together to define, frame and understand problems, and ultimately, come to solutions. It is, consequently, the effectiveness of the governance ‘set-up’ for such interactions that is studied under Task 2.4. The outputs of this social process in the shape of policy interventions and management measures, affecting e.g., fisheries practices, are typically evaluated through indicators for performance vis-à-vis ecology, economy, and social aspects. However, for governance, a fourth set of attributes and indicators has to come into play.In responding to this, the evaluation of the effectiveness of fisheries governance in the EU is informed by two sources: i) the approach taken by the Canadian Fisheries Research Network (CFRN) (reported in Stephenson et al. 2017a, 2017b, 2018), which constitutes a framework for comprehensively evaluating fisheries and includes potential performance indicators selected from a comprehensive review of the literature (comparing a wide variety of existing frameworks); and ii) the Aquaculture Governance Indicators (AGIs) framework (Toonen at al. 2021), which not only provides a comprehensive governance framework but operationalizes it via scoring of specific indicators and criteria across the core components of governance.The CFRN framework operates with three overall institutional (or governance) objectives: a) legal objectives, b) good governance structure, and c) effective decision-making processes, which are then further decomposed into attributes of these objectives. As an example, attributes under ‘effective decision-making processes’, include (among others) concerns around participation, transparency, structuredness, and integration. The CFRN framework goes on to suggest candidate indicators for these attributes. The CFRN framework are supplemented with insights from the AGIs framework, which serves to provider further insight in how to go from specific indicators and to criteria that these can be measured according to.In the final section, a plan for further work under Task 2.4 is outlined. The plan involves focussing on both the overall regional governance arrangements in the Baltic Sea, North Sea, Western Waters and the Mediterranean Sea, and in-depth studies of sub-regional cases of fisheries governance in the same regions. The analysis of the cases will result in recommendations on how the effectiveness of governance could be improved.Using the governance elements outline in the first section of the deliverable as a lens to approach the research undertaken under Task 2.4, a two-pronged research plan allowing comprehensive understanding of the general nature and quality (i.e., how effective) of governance arrangements is outlined in the final section. The first prong is through the implementation of an expert elicitation survey, which is comprised of specific governance-related questions that is sent to the diverse range of governance actors across sectors (academia/research, civil society, government) for each of the SEAwise regional seas (at regional level). The second prong consists of a selection of in-depth sub-regional case studies informed by SEAwise partners based on their experiences and existing knowledge while also leveraging their research network to inform the details of the case study through for instance interviews.The outlined research plan is designed to deal with multi-level dynamics of fisheries governance (level of the regional seas and sub-regional level) and can accommodate the study of ‘socially acceptable’ management measures, and how social acceptance of management measures might be increased by considering increased use of self-governance (or other improvements to the governance arrangement studied).More information about the SEAwise project can be found at https://seawiseproject.org/

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.104
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0060.004
Open science0.0020.005
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.0260.013

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.

Opus teacher head0.101
GPT teacher head0.352
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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