Exploring the balance of ecological, economic, governance, and social considerations in marine protected area network evaluations
Bibliographic record
Abstract
Marine protected area networks (MPANs) are a critical tool at the forefront of global marine biodiversity conservation and sustainable development agendas. MPANs are complex tools that seek to provide important ecological and human benefits. The Convention on Biological Diversity “Aichi Targets” were developed to safeguard biodiversity and enhance benefits for people through sustainable use. Target 11 (draft 2030 action Target 3) describes elements of the key (environmental, economic, governance, social) dimensions associated with MPANs from a global perspective. Understanding the balance of these interrelated dimensions in MPAN evaluations is critical to developing future conservation strategies that can adapt to changing contexts and conditions. This dissertation draws on Aichi Biodiversity Target 11, and its associated multidimensional foundation to understand how MPANs are evaluated toward their global targets. The research herein was grounded in this multidimensional context to offer insight into how MPANs have been evaluated. I performed a systematic literature review to understand the indicators used to evaluate Aichi Target 11 qualitative elements. Results showed that the qualitative elements were unevenly evaluated in MPAN literature. I then conducted a two-part online survey to characterize attributes of ecological, economic, governance, and social dimensions considered in MPAN evaluations, and identify the indicators used to evaluate them. Survey results indicated that MPANs with both biodiversity and socially-oriented objectives considered a larger suite of attributes in their evaluations than those without social considerations, without de-emphasizing ecological considerations. In practice, attributes were informed by a suite of indicators with varied composition, unlike the single, attribute-specific indicators identified in the literature. This dissertation aligned with an increased interest in MPANs that go beyond a focus solely on biodiversity conservation to encompass sustainable models, which incorporate socially-oriented objectives. The findings revealed limited use of approaches that holistically assess MPANs. Existing practices tend to be biased towards ecological and governance dimensions. Future research is needed to identify attributes and indicators to help elucidate challenges from all dimensions, and in every part of the MPAN process, from design through evaluation.
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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 | 0.196 | 0.299 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".