Soft vs. Hard Sustainability Approach in Marine Spatial Planning: Challenges and Solutions
Bibliographic record
Abstract
Hitherto, over 50% of countries with marine waters have established MSPs or launched related legal actions. However, there are still conceptual and practical challenges to be overcome in the development of MSP. In this study, we investigate two main approaches in MSP (hard vs. soft sustainability) through reports, published manuscripts and meeting proceedings in seven pioneering countries (Belgium, Netherlands, Norway, Germany, United Kingdom, Australia, and Canada). We highlight the gaps, challenges, and solutions in each of these approaches. From our findings, there are four common challenges in both soft and hard sustainability approaches as follows: (i) the political framework and inconsistent support of MSP efforts, (ii) insufficient knowledge on social dimensions, (iii) insufficient stakeholder engagement in the diversity of stakeholder’s groups or in their contribution to the planning process from the initial steps, and (iv) finding a balance between environmental conservation and economic growth. We recommend that future studies should investigate how MSP can become more adaptive to long-term environmental and economic targets, how effective involving socioeconomic strata is in MSP, and how decision-making tools could help to cover the gaps in MSP. Furthermore, public forums are suggested to be developed to facilitate the systematic sharing of MSP experiences worldwide.
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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 | 0.035 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".