Fishing livelihoods as key to marine protected areas: insights from the World Parks Congress
Bibliographic record
Abstract
Abstract Marine protected areas (MPAs) have become a widely used tool for marine conservation and fisheries management. In coastal areas, it has become clear that the success of MPAs, and the achievement of sustainable fishery production, requires a combination of effective management and conservation frameworks, maintenance of decent fisheries livelihoods, and a governance system that allows for effective participation of coastal communities, fishing people, and other ocean users in considering, designing and implementing MPAs. These ingredients are crucial to provide the social sustainability needed to achieve ecological sustainability, and in particular, to reconcile fisheries and marine conservation objectives, in light of the United Nations Sustainable Development Goals and Aichi targets of the Convention on Biological Diversity (CBD). Since its inception in 1962, the series of World Parks Congresses (WPC) has focused on protected areas, in both terrestrial and marine domains. The 2014 WPC in Sydney reinforced the apparent movement, started at the Durban WPC of 2003, towards recognition of social and economic issues related to MPAs, including the importance of food security and livelihoods, and the crucial nature of interactions between MPAs and fisheries. Many discussions at the 2014 WPC focused on these human dimensions of MPAs, and the need to incorporate them into MPA decision‐making. This article examines the process and outcomes of the 2014 WPC, with emphasis on the role of people (in particular, fishers) in marine conservation, and particularly in coastal MPAs. In doing so, the article examines the process of producing a Marine Statement at the end of the WPC, as a component of the final ‘Promise of Sydney’ declaration. That process led to a range of concerns including (i) issues over transparency and inclusiveness in the statement's development, and (ii) content issues focused on representation of the social and economic conclusions, and advocacy of a specific MPA target for no‐take areas. The article focuses on potential strategies for moving constructively beyond the still existing tensions between environment‐ and people‐focused conservation and development. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".