The Marine Spatial Planning Index: a tool to guide and assess marine spatial planning
Bibliographic record
Abstract
Marine spatial planning (MSP) has the potential to balance demands for ocean space with environmental protection and is increasingly considered crucial for achieving global ocean goals. In theory, MSP should adhere to six principles, being: (1) ecosystem-based, (2) integrated, (3) place-based, (4) adaptive, (5) strategic, and (6) participatory. Despite nearly two decades of practice, MSP continues to face critical challenges to fully realize these principles, hindering its ability to deliver positive outcomes for people and nature. Here, we present the MSP Index, a tool for assessing progress in MSP processes based on MSP principles that can guide practitioners in operationalizing these principles. Using qualitative analysis of fundamental MSP guides, complemented with a literature review, we identified key features of MSP principles and developed these features into a scoring guide that assesses progress relative to each principle. We trialed and validated the MSP Index on six case studies from distinct regions. We found that the MSP Index allows for high-level comparison across diverse marine spatial plans, highlighting the extent to which MSP principles have permeated practice. Our results reveal successes, especially for the place-based principle, and failures to fully adhere to the adaptive and participatory principles of MSP. The Index serves as a guidance tool that would be best employed by practitioners and can inform science on the evolution of MSP. It is a user-friendly tool that translates MSP principles into practice, allowing for assessment of individual initiatives and comparison of diverse initiatives across ocean regions and nations.
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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 | 0.028 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".