Why people matter in ocean governance: Incorporating human dimensions into large-scale marine protected areas
Bibliographic record
Abstract
Large-scale marine protected areas (LSMPAs) are rapidly increasing. Due to their sheer size, complex sociopolitical realities, and distinct local cultural perspectives and economic needs, implementing and managing LSMPAs successfully creates a number of human dimensions challenges. It is timely and important to explore the human dimensions of LSMPAs. This paper draws on the results of a global “Think Tank on the Human Dimensions of Large Scale Marine Protected Areas” involving 125 people from 17 countries, including representatives from government agencies, non-governmental organizations, academia, professionals, industry, cultural/indigenous leaders and LSMPA site managers. The overarching goal of this effort was to be proactive in understanding the issues and developing best management practices and a research agenda that address the human dimensions of LSMPAs. Identified best management practices for the human dimensions of LSMPAs included: integration of culture and traditions, effective public and stakeholder engagement, maintenance of livelihoods and wellbeing, promotion of economic sustainability, conflict management and resolution, transparency and matching institutions, legitimate and appropriate governance, and social justice and empowerment. A shared human dimensions research agenda was developed that included priority topics under the themes of scoping human dimensions, governance, politics, social and economic outcomes, and culture and tradition. The authors discuss future directions in researching and incorporating human dimensions into LSMPAs design and management, reflect on this global effort to co-produce knowledge and re-orient practice on the human dimensions of LSMPAs, and invite others to join a nascent community of practice on the human dimensions of large-scale marine conservation.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".