Striking a balance between ecological, economic, governance, and social dimensions in marine protected area network evaluations
Bibliographic record
Abstract
Abstract Marine protected area networks (MPANs) are promised as tools for protecting biodiversity and contributing to sustainable development. The variety of expected social‐ecological outcomes associated with MPANs underscores a need to consider ecological, economic, governance, and social dimensions in MPAN design, implementation, monitoring, and evaluation. However, little is known about how these four dimensions are considered or shaped by objectives. We conducted an online survey with MPAN managers, technical staff, and academics from across the globe (77 survey responses that described 48 MPANs located in 59 countries). Our findings confirmed that most MPANs have various co‐occurring, potentially conflicting objectives. MPANs with biodiversity and societal objectives considered attributes (e.g., human well‐being and economic distribution, institutional partnerships, and network‐specific ecological attributes) among all dimensions, with greater frequency than MPANs with only biodiversity objectives. Nonetheless, ecological attributes were always perceived as important irrespective of the MPAN objective. Reaching synergies between the multiple dimensions of MPANs can be challenging if dimensions get overlooked in MPAN evaluations. Identifying the important attributes considered in MPANs offers insight into the practice of MPAN design, implementation, monitoring, and evaluation and can help improve MPAN success.
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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 | 0.247 | 0.304 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".